A Project Draft On “Policy Interest Rates, Market Rates, Inflation and Economic Growth”

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A Project Draft
On
“Policy Interest Rates, Market Rates, Inflation and Economic
Growth”
Project Investigator Team
Dr. Charan Singh Arvinder S Sachdeva
CEO and Director, EGROW Foundation Former Senior Advisor, GoI
998, Sector 29, Arun Vihar, Balbir Kaur
Noida- 201301, UP-IndiaFormer Advisor, RBI
Dr. Pabitra K Jena
Assist Prof, SMVD University
The study has benefitted from discussions with Prof. Ashima Goyal (IGIDR), Prof. Bandi
Kamaiah (University of Hyderabad), Prof. N R Bhanumurthy (NIPFP and BASE, Bengaluru),
Prof. Vighneswara Swamy (IBS), Prof. Lokendra Kumawat (Delhi University), Dr. C
Rangarajan (former Governor, RBI), S K Hota (MD, National Housing Bank), Mohan Tanksale
(former CMD, Central Bank of India), S S Kohli (former CMD, Punjab National Bank), Udaya
Kumar (MD and CEO, Credit Access Grameen Koota), Subrata Gupta (Former MD, NABARD
Financial Services), Pillarisetti Satish (Executive Director, Sa-Dhan).
The econometric contribution by Prof Vighneswara Swamy and Prof. Lokendra Kumawat, is
also thanfully acknowledged.
1 Section 1
Introduction
Monetary policy, as part of macro-policy, impacts economic growth and financial stability. The
Reserve Bank of India (RBI) operates monetary policy through interest rates to finally impact
inflation and economic growth. The extent to which monetary policy intervention affects the real
economy has been a central theme in academic studies and public policy. Being a key indicator
of financial markets, interest rates have a strong impact on the economy. To identify the
transmission mechanism of monetary policy, operated through interest rate, on economic growth,
is a challenging task faced by policy-makers and academics.
Interest rate is a unique instrument which impacts many sectors. A higher interest rate can deter
investment but attract the much needed capital flows for growth which can cause exchange rate
to appreciate and adversely impact exports. Also, in a fiscally constrained country, cost of
borrowing tends to rise with increasing interest rates which further acts as a drag on growth of
the economy through curtailing investment, both in public sector and through crowding out, in
private sector. Investments, in particular, can show considerable sensitivity to variations in
interest rates though it can be argued that other variables, like uncertainty, also play a role in
investment decisions.
The last several years have witnessed greater reliance on monetary policy instruments to bring
about stabilisation in output levels and controlling rate of inflation, especially since 2008. This
has been particularly the case in most of the advanced economies, which have witnessed low
inflation (generally lower than the mandated target), and who despite pursuing loose monetary
policy for an extended period continue to experience low levels of inflation. One issue that has
been raised in recent literature relates to effectiveness of unconventional monetary policy since
2008, in particular when interest rates are very low - often close to zero or even in the negative
zone, and persistently so. Unconventional monetary policies are again being followed because
of Covid-19. However, it’s too early to assess about its transmission and impact on growth,
investment, inflation, etc.
2 The situation in developing countries and more so in India, however, has been somewhat
different. India, witnessed close to double-digits annual average increase in the price level in the
early years of the decade starting in 2010. However, since 2013-14, the inflation rate has
declined to an average of less than 5 percent per annum. Of course, the reduction in inflation is
not attributable to monetary policy alone and a number of other factors have played a role.
However, of late and more so, ever since India formally adopted 'inflation targeting' in 2016 as
one of key mandates of the Reserve Bank of India, monetary policy has come to centre-stage for
controlling inflation.
The effectiveness of monetary policy depends on the overall policy environment within which an
economy functions. The liberalisation of financial markets in India since the early 1990s has
proceeded at a gradual pace and has been characterised by permitting new banks to join, creation
of new markets, and strengthening of money and G-Secs market.
The above mentioned factors, apart from many others, tend to have an impact on the
transmission mechanism of the measures adopted by monetary authorities. In India, for example,
large requirements on banks to hold government securities and persistently high fiscal deficit
(Centre and States) have an impact on transmission of monetary policy measures to market rate
of interest. However, greater economic and financial integration with the rest of world in the
form of liberalisation of capital account, higher capital inflows, and flexible exchange rates, pose
challenges to the effectiveness of monetary policy.
Most of the literature in the context of monetary transmission in India seems to suggest that there
is limited pass-through from policy rates to deposit and lending rates, inflation and output.
Monetary policy also affects the exchange rate but transmission from exchange rate channel to
output and inflation also appears muted.
It can be argued that monetary transmission in the recent period was reasonably swift across
various money market segments, given the directions by the RBI since 2014. However, the
3 transmission to bank deposits and lending has been delayed and partial. The fact is that most of
the lending is contracted at floating rates while most of the deposits are contracted at fixed
interest rates. This asymmetry tends to impede the transmission to lending rates. In addition,
competitive pressure from mutual funds and small savings schemes have also impacted
transmission mechanism.
In order to improve the transmission from policy rates to other market rates (borrowing and
lending rates), the Reserve Bank of India has recently shifted from marginal cost of funds based
lending rates (MCLR) regime to external benchmarking of lending rates. Accordingly, the
Reserve Bank has mandated all scheduled commercial banks (excluding regional rural banks) to
link all new floating rate loans to micro and small enterprises to an external benchmark.
Accordingly, with effect from October 1, 2019, commercial banks were given the freedom to
choose any of the following external benchmarks - a) RBI's Policy Repo Rate, b) Government of
India 3/6 month Treasury Bill yield published by Financial Benchmarks India Private Ltd
(FBIL), and c) Any other benchmark market interest rate published by FBIL. Early indications
seem to suggest that there has been an improvement in transmission to fresh loans sanctioned in
the sectors where new floating rates have been linked to external benchmarks. This is because,
unlike the MCLR system where transmission to lending rates was dependent on changes in
deposit rates, the transmission to lending rates under external benchmarks system is not
contingent upon interest rates on deposits.
Research Question or Hypothesis
The objective of the present study is to identify linkages between policy interest rate, and
economic growth, aggregate investment, and inflation,.

Section Scheme
After this Introduction, in Section 2 the literature on subject of transmission mechanism of
monetary policy is reviewed. This is done both in the context of developed as well as developing
countries with special focus on monetary transmission in India. Section 3 begins by discussing
4 the evolution of the operating framework of monetary policy in India. The framework has
evolved since the introduction of the Prime Lending Rate in 1994 till the adoption of the new
monetary policy framework, inflation targeting, in 2016. In 2019, several of the existing loan
products have been linked to the Repo Rate with the intention of improving monetary policy
transmission in the economy. In Section 4, the Research Methodology used in the study which
includes the empirical estimation techniques to examine the different mechanisms of monetary
policy transmission in India is discussed. In Section 5, quantitative results are discussed after a
brief trend analysis. In this section, impulse response functions, and SVAR estimation has been
used that attempts to investigate the relationship between Repo rate, and private corporate
investment, inflation, asset prices and GDP growth. Finally, in the next section, broad
conclusions that emerge from the study, and recommendations are presented.
5 Section 2
Review of Literature
The objective of monetary policy, as was nearly universally accepted until 2008, was to achieve
price stability with the objective of ensuring sustainable economic growth. Since 2008, after
global financial recession, even financial stability has been included in the objectives of
monetary policy. Thus, in the current context, the efficacy of monetary policy actions lies in the
speed and magnitude with which they achieve the final objectives of price stability while
considering growth and financial stability.
The literature on transmission of monetary policy is very vast, and has been extensively
examined, especially in context of advanced countries. The literature covers the relationship
between monetary policy & growth, and inflation, as well as transmission mechanism through
various channels. To have an efficient transmission mechanism, it would be necessary to have
healthy banks, well developed financial markets, market determined interest rates and robust
payment and settlement system (Acharya, 2020). The transmission mechanism is characterized
by time lags that tend to differ because of differences in economic and market structures in
different countries.
1
These lags vary from 1-14 quarters across advanced and emerging
economies. In EMEs, transmission is generally weaker and lags are generally shorter - average
lag of 33.5 months for all countries as compared with 42 months in the case of the US, 48
months for the euro area, and in the range of 10-19 months for transition economies that became
new EU members (Havránek and Rusnák, 2012). In Brazil, monetary policy transmission
through aggregate demand channel takes between 2 and 3 quarters: the interest rate affects
consumer durables and investment in between 1 and 2 quarters, and the output gap takes another
quarter to impact inflation (Bogdanski et al., 2000).
1 RBI (2014) explains in detail about the lags.
6 The section also presents a discussion on literature on transmission mechanism in India. A brief
review of literature is presented in this section and more focused, in tabular format, is placed in
Annexure 1.
2.1: Monetary Policy and Investment
An important aspect considered across literature has been the role played by the cost of capital,
or the interest rate in determining the level of investment. This makes it critical to investigate
different channels through which monetary policy can have an impact on the aggregate economy
by influencing the decision of the firm to invest. There have been several approaches that have
been adopted to explain the decision of a firm to invest. Some of these theories take a
macroeconomic perspective on the issue while there are several micro-founded firm level
behaviour explanations that have also been provided to explain the investment behaviour across
several countries.
It has been argued that despite several monetary policy measures taken in advanced economies
since the financial crisis, the global economic recovery has been slow and a major reason behind
this has been the subdued pace of investment activity. It is commonly considered that four
factors have been considered as potential drivers of investment at a macroeconomic level,
namely demand expectations, financial conditions, uncertainty and supply shocks. Monetary
policy typically affects financial conditions and has an impact on demand expectations as the
transmission to the real economy is often through the investment channel. Since the financial
crisis of 2008 these factors have been central to the debate on investment. In particular, despite
aggressive and prolonged period of unconventional monetary policy combined with record low
levels of interest rates, the economic prospects in many countries continued to remain weak.
Moreover, weakening economic prospects globally are expected to lead to a decline in the
returns on investment, thereby dampening the formation of new capital and delaying the
replacement of old capital. Uncertainty may also have persistently negative effects on business
investment. Finally, unexpected negative supply shocks, such as the fall in labour productivity
across countries, could diminish future profit expectations and lead to a decline in investment
activity.
7 There have been several different approaches that have been adopted to explain the decision of a
firm to invest. Chatelain et al. (2003) focused on rich datasets for Germany, France, Italy, and
Spain and estimated investment behaviour using user costs, sales, and cash flow. The key result
was that investment is sensitive to cost of capital. The findings were consistent with the study by
Mojon, Smets & Vermeulen (2002). In contrast, Eberly (1997) argued using firm level data from
11 countries that there were nonlinearities present due to presence of different fixed or non-
quadratic costs. These non-linearities were present between the investment and fundamentals.
Cuthbertson & Gasparro (1995) examined the neoclassical intertemporal framework where
Tobin’s marginal Q determines the real investment level. They found that investment was
dependent on average Q, capital gearing and output which was then used to explain the fixed
investment in UK’s manufacturing sector between 1968 to 1990, using an error correction
model.
Sharpe and Suarez (2015) explorde the reasons behind the mixed evidence of the impact of
interest rates on investments. They used a survey of Chief Financial Officers (CFOs) of different
companies to study the sensitivity of investment plans and find that decreases in interest rates
have little impact on investment decisions but any increase in interest rates has a significant
impact on investment. Their results indicated that CFOs either mention adequate cash as the key
factor for the lack of sensitivity of interest rates on investment. They further found that this
insensitivity is more for firms that don’t have financial constraints or firms with no near term
plans to borrow while investment is also insensitive to interest rate changes for firms that expect
a higher growth rate in the coming year.
Hambur and Cava (2018) analysed the investment behaviour of firms for Australia by compiling
a dataset that allows them to study the distribution of borrowing costs and the relationship
between cost of capital and fixed capital investment. They found a high degree of heterogeneity
in cost of capital which has increased post-2008 as good companies are able to raise capital
cheaply while the cost of capital for the bottom companies has increased significantly. They used
the distribution of borrowing costs and find a significant inverse relationship between the cost of
borrowing and corporate investment. Ottonello and Winberry (2019) considered the role of
financial frictions as they attempted to study the investment channel. They found that companies
8 with low debt burdens and high credit ratings tend to respond more to monetary shocks. This
finding is then interpreted using a New Keynesian model with default risk. Their model shows
that the relatively flat marginal cost of financing of investment for low risk firms enables them to
be more responsive to monetary shocks.
Jobst & Lin (2016) examined negative interest rates in the Euro-zone and found that the negative
interest rates resulted in easing financial conditions along with a modest expansion in credit.
They argue that the zero lower bound is thus less binding as originally imagined. However, they
discuss that substantial rate cuts may end up outweighing the benefits from higher asset values
and stronger aggregate demand.
Agarwal and Kimball (2019) explored the possibility of deep negative interest rates to combat
economic recessions. They argue that Central Banks have the power to enable deep negative
rates whenever needed which maintains the power of monetary policy in future to address output
gaps in a short time. They discuss the factors that explain how standard transmission
mechanisms from interest rate cuts to aggregate demand remain unchanged in the deep negative
rate territory.
The important finding across literature is that generally interest rates have an impact on the
aggregate economy through the investment channel.
2.2: Monetary Policy and Inflation
One issue that has been covered extensively in literature has been the relationship between
interest rate and inflation. Despite historically lower levels of interest rates, central banks have
consistently undershot their inflation targets since the 2008 financial crisis in a number of
advanced economies. This makes it important to look at the relationship between interest rates
and inflation – more so, how it has evolved over time. The relationship between policy interest
rate and inflation has been studied extensively in the literature.
Pennacchi (1991) looked at the dynamics of real interest rates and rates of inflation expectations
in the context of an equilibrium asset pricing model. Considering the real interest rates and
inflation to be mutually dependent processes, there is a strong evidence of a negative and
significant correlation between real interest rates and expected inflation.
9 Crowder and Hoffman (1996) examined the long run relationship between interest rates and
inflation. They find strong evidence in support of the traditional “tax adjusted” Fisher equation
and find that a one percent increase in inflation results in a 1.34 percent increase in nominal
interest rates. Post tax effects, the Fisher effect is the same as unity which is consistent with the
conventional Fisher equation.
Cochrane (2016) highlighted that the standard “New-Keynesian” model worked well for
explaining the stability of inflation even at a zero-interest rate peg. Christensen and Spiegel
(2019) examined Japan’s negative policy rates which were introduced in 2016 and argued that
market expectations for inflation over the medium term fell immediately. The reaction indicates
the uncertainty which has been around the efficacy of negative policy rates as a tool to stimulate
economic growth when inflation expectations are anchored at lower levels. They further mention
the desirability of pre-emptive measures to avoid a situation of the zero-interest rate bound.
Frankel (2006) finds that the relationship between real interest rates and real commodity prices is
empirically supported (Annexure 1).

Bhalla (2018) noted that inflation in the US averaged 1.9 percent between 1996 and 2009 – and
that in the next 8 years it averaged 1.2 percent. He further mentioned that world growth has
moved inversely with world inflation and argues that output gap does not explain the moderation
in inflation. The contention is that the decline in share of working age population is consistent
with the structural decline in inflation and he argues that the excess global supply of college
graduates due to expansion of education has resulted in stagnation of wages. This, in turn, has
kept wages low resulting in a structurally lower level of inflation despite an accommodative
monetary policy that has been adopted by several countries post the Global Financial Crisis in
2008.
2.3: Channels of Transmission of Monetary Policy
Monetary policy transmission occurs through several alternative channels, viz., interest rate,
credit, exchange rates, and asset prices (Mishkin, 1995). In the recent literature, expectations
channel has also been mentioned, but that has not been explored in this study.
10 Interest rate channel
With the deepening of financial systems and growing sophistication of financial markets, most
central banks are increasingly using indirect instruments rather than direct measures.
Adjustments in policy interest rate, for instance, directly affect short term money market rates
which then transmit the policy impulse across the financial system, including deposit and lending
rates. Eventually, consumption, saving and investment decisions of economic agents and
eventually aggregate demand, output and inflation are impacted. The interest rate channel of
transmission has become the cornerstone of monetary policy in most countries. Mohanty and
Turner (2008) argued that credible monetary policy frameworks put in place across EMEs in
recent years have strengthened the interest rate channel of monetary policy transmission.
In the case of advanced economies (AEs), the interest rate channel works by impacting the cost
of capital. This channel has been found to be strong, and has exhibited good information content
about future movement of real macroeconomic variables (Bernanke and Blinder, 1992). In the
case of EMEs, which do not have well-functioning and integrated capital markets, and in which
other markets are fragmented and relatively illiquid, monetary transmission through the interest
rate has been found to be relatively weak. Furthermore, the interest rate channel is also rendered
weak during surges in capital inflows. On an average, the pass-through coefficients for
transmission from policy rates to lending rates across Asian economies declined by about 30-40
basis points during episodes of capital inflows (Jain-Chandra and Unsal, 2012). Transmission
from policy rates to money market rates and retail lending rates was found to be strong in
transition economies of Europe, but the transmission to longer maturity rates was somewhat
weak (Égert and MacDonald, 2009).
Mukherjee and Bhattacharya (2011) found that the interest rate channel impacted private
consumption and investment in EMEs, with and without inflation targeting (IT). Their results
suggest that interest rates have significant impact on private sector activity both in inflation
targeting emerging market economies and potential inflation targeters in MENA region. The
estimates show that the real interest rates have statistically significant and negative impact on
private investment in both group of countries (0.662 in IT EMEs and 0.029 in non-IT MENA
11 EMEs). In Sri Lanka, Amarasekara (2008) found interest rate channel to be important for
monetary policy transmission.
Acosta-Ormaechea and Coble (2011), compared the monetary policy transmission in dollarised
and non-dollarised economies found that the interest rate channel in terms of real rates affecting
investment was found to be more important in Chile and New Zealand. Gumata et al. (2013)
attributed strengthening of the interest rate channel in many EMEs to reduced fiscal dominance,
more flexible exchange rates and development of market segments.
Credit Channel
The credit channel of monetary transmission operates through both the bank lending channel and
the balance sheet channel (contractionary monetary policy decreases collateral valuation and net
worth of firms, raises agency costs and affects firms’ activity levels). Mishra, Monteil and
Sengupta (2016) find that the monetary transmission through bank lending channel is carried out
in two stages- from policy rates to bank lending rates and from bank lending rates to aggregate
demand (Annexure 1). Evidence from the euro area suggests that the bank lending channel was
more pronounced than the balance sheet channel in the case of firms, while for households, it
was the other way round (Cicarrelli, et al, 2010). The bank lending channel is also found to have
a larger impact on banks that are small, less capitalised and less liquid. Some evidence suggests
that firms substitute trade credit for bank loans at times of monetary contraction, thus weakening
the credit channel. This is particularly the case for EMEs.
Takáts et al (2013) find that declining bank credit to the private sector will not necessarily
constrain the economic recovery after output has bottomed out following a financial crisis. From
39 financial crises, which – as the one in 2008-09 – were preceded by credit booms, they suggest
that in these crises the change in bank credit, either in real terms or relative to GDP, consistently
did not correlate with growth during the first two years of the recovery. In the third and fourth
year, the correlation becomes statistically significant but remains small in economic terms. The
lack of association between deleveraging and the speed of recovery does not seem to arise due to
12 limited data. In fact, data shows that increasing competitiveness, via exchange rate depreciations,
is statistically and economically significantly associated with faster recoveries.
Deteriorating bank balance sheets due to crisis-induced credit losses could have made it difficult
for some banks to meet the minimum capital requirements and expand credit supply, as issuing
new equity (given the scarcity of capital and heightened investor risk aversion) or cutting
dividends proved difficult and costly (Borio and Zhu, 2012). Weakened bank balance sheets
limited the supply of credit during the 2008 crisis (Foglia et al., 2010; Holton et al., 2012; and
Puri et al., 2011). The size of non-performing assets indeed increased at the beginning of the
crisis and did not decline substantially until late-2012 in a number of countries, especially those
where house prices dropped substantially (the United Kingdom, the United States and some euro
area countries). Even so, the extent of non-performing loans has risen surprisingly little so far in
some euro area countries (OECD, 2012, 2013).
In the case of Sub-Saharan Africa, excluding South Africa, the bank lending channel has been
found to work feebly, given that informal finance dominates credit markets and the penetration
of institutional finance is limited, leading to low competition from the banking sector. However,
in the case of many EMEs, especially where bank-oriented financial systems exist, the credit
channel has remained strong. Using the VAR framework, Disyatat and Vongsinsirikul (2003)
found that in Thailand, in addition to the traditional interest rate channel, banks played an
important role in monetary policy transmission mechanism, while exchange rate and asset price
channels were relatively less significant. For the Philippines, Bayangos (2010) found the credit
channel of monetary transmission to be important. Ncube and Ndou (2011) showed that
monetary policy tightening in South Africa can marginally weaken inflationary pressures
through household wealth and the credit channel. While informal finance weakens monetary
transmission, the credit channel remains important in the case of micro-finance institutions.
Exchange Rate Channel
An important channel of monetary transmission has been the exchange rate that is either directly
influenced by the central bank or gets impacted by its actions. Typically, the exchange rate
channel works through expenditure switching between domestic and foreign goods. For instance,
13 contractionary monetary policy would lead to higher interest rates and consequent appreciation
of the domestic currency making foreign goods cheaper causing demand for domestic goods and
net exports to fall resulting in a decline in output.
However, this may also reduce external debt in domestic currency terms. Both effects transmit to
aggregate demand and the price level. Empirical evidence suggests that the exchange rate
channel is strong in economies with freely floating exchange rates, but its impact is dampened in
case central bank intervenes in the foreign exchange market. For instance, in the case of Latin
American countries, lower exchange rate flexibility relative to their peers in Asia seems to have
resulted in weaker transmission of policy rates. Acosta-Ormaechea and Coble (2011), comparing
the monetary policy transmission in dollarised and non-dollarised economies found that the
exchange rate channel played a substantial role in controlling inflationary pressures in Peru and
Uruguay. Hnatkovska, Lahiri and Vegh (2008) find that relationship between interest rates and
exchange rates is non-monotonic (Annexure 1).
Asset Price Channel
Apart from exchange rates, changes in other asset prices such as equities and house prices also
impact inflation and growth. Equity prices are dampened in response to contractionary monetary
policy and the resultant wealth effects and collateral valuation changes feed through to
consumption and investment. The asset price channel is quite weak in many EMEs where equity
markets are small and illiquid, but relatively strong in countries that have well developed equity
markets. Transmission is also found to be limited in countries with weak property price regimes
and poorly developed and illiquid real estate markets. In countries like the US and Australia,
where the mortgage market is well integrated with capital markets, the asset price channel turns
out to be quite strong. In general, stock prices respond faster to contractionary monetary policy,
though the intensity and lags of transmission are impacted by the liquidity in the stock markets.
Horatiu (2013) observed a significant impact of asset prices on both consumption and
investment, two economic actions that can help the economy. Mahat and Abdullahi (2015)
14 established that the asset price channel of monetary transmission mechanism in Kenya is not
effective. Shah, Chen, Shafi, and Shah (2015) find that stock prices have a negative long run
relationship with investment and output. Jones and Bowman (2019) found that the pass-through
of short-term repo rate shocks to asset prices and real activity appears stronger compared to
money supply shocks. Nombulelo, Kabundi and Ndou (2013), found that a rise in the short-term
rate affects demand for stock negatively and consequently stock prices drop. The all-share index
does not react upon impact, and eventually decreases gradually, attaining the lowest level of 0.32
percent after 2 quarters. House prices do not react contemporaneously, but the effect is
statistically significant, reaching a minimum value of 0.08 percent after two quarters.
2.4: Experience of Unconventional Monetary Policy
In the wake of global financial crisis (GFC) many Central banks had to depart from what can be
termed as conventional monetary policy (because of the failure of the financial system to respond
adequately to it) to adopt unconventional monetary policy tools. The unconventional monetary
policy includes, among others, negative interest rates, expanded lending operations, assets
purchase programmes and forward guidance.
One issue that has been raised in the recent literature relates to effectiveness of unconventional
monetary policy with near zero rates of interest. Borio and Hofmann (2017) suggested that, "-
both conceptually and empirically there is support for the notion that monetary policy is less
effective when interest rates are persistently low." This was on account of two reasons - "(i)
headwinds that typically blow in the wake of balance-sheet recessions when interest rates are low
(e.g. debt overhang, an impaired banking system, high uncertainty, resource misallocation)."
And, "(ii) Inherent non-linearities linked to the level of interest rates (e.g. impact of low rates on
banks' profits and credit supply, on consumption and savings behaviour - and on resource
misallocations)." There is evidence that the headwinds experienced from recovery from balance-
sheet recessions may deter the effectiveness of monetary policy and that lower rates can impact
consumption as well as credit. A high level of uncertainty may lead to risk aversion, which may
dampen the impact of lower interest rates (Kamiah, 2020).
15 Negative interest rates
In recent years, especially after the global financial crisis (GFC) of 2008, some countries have
experimented with negative interest rates. Prior to GFC, it was widely believed that there was a
‘zero lower bound’ for the policy interest rate, implying that nominal interest rates could never
be negative. This was because if interest rates were negative, people would simply choose to
hold their savings in cash and deposits would be unavailable to banks for lending or other
purposes. However, post GFC some countries that have negative policy rates include Sweden,
Denmark, Switzerland, European Central Bank, Hungary, Norway and Japan. Bean and Broda et.
al. (2015) hypothesize that a higher propensity to save in the world along with a lower propensity
to invest and increasing demand for safer risk-free assets has been putting the downward
pressure on interest rates. Carlos and Kose et al (2016) mention that the transmission channels
have worked as expected, during the negative interest rate regime, through the interest rate,
credit, and exchange rate channels. Torsten (2016) mentions that the confidence costs of negative
interest rates outweigh its small economic benefits and for a majority of banks negative interest
rates have seen no uptick in lending volumes. Moreover, due to negative yields in the Euro area,
investors preferred the US markets where yields were still attractive. Andreas and Lin (2016)
discuss about the pass-through of negative interest rates on the economy and their impact on
lifting inflation and aggregate demand. Potter and Smets (2019), observe that the policy of
negative nominal interest rates along with other unconventional monetary policy measures had a
reasonably strong impact in terms of reducing government bond yields as well as yields on
corporate debt. They may have also helped in raising stock prices. However, the pass-through to
retail deposit rates appear to have a floor of zero because of the possibility of shifting to cash.
Expanding Lending Operations
In recent years, since 2008, after the GFC, a number of central banks introduced new lending
measures or adjusted existing ones in order to improve liquidity mainly in the short-term money
markets. More such measures were introduced to provide monetary accommodation during
2010-16. Central banks increased the frequency of repo auctions, provided funds for longer
maturities, increased the range of acceptable collaterals, and broadened the set of institutions that
could participate in monetary operations. Potter and Smets (2019), summarised vast literature,
and concluded that these measures helped in easing liquidity strains, restore monetary
16 transmission channels, eased funding conditions for non-financial corporations and households.
The unconventional measures were largely successful in supporting stronger growth and higher
inflation. However, the effects of these measures were heterogeneous across Euro area, with
countries that had a more fragile banking system benefitting less. Studies from several Euro
countries suggest that ECB’s long-term refinancing operations (LTRO) increased credit supply
to non-financial corporations and targeted LTROs resulted in faster lending growth and lower
lending rates.
Large Scale Asset Purchase Programmes (APP)
Large scale APPs were another measure adopted by the central banks in some countries to
address the disruptions in the transmission mechanism of monetary policy and provide additional
monetary stimulus. The instruments purchased included covered bank bonds, corporate bonds,
commercial paper, agency mortgage-based securities, other asset- based securities, real estate
investment trusts, exchange-traded funds and public sector bonds. Central banks mainly
purchased public sector issued securities, although in some cases they also purchased other
securities. These operations were generally large scale and lasted for long period. Most countries
that undertook large scale asset purchase programmes reported a reduction in bond yields to
varying degrees. These also helped in lowering lending rates. Several studies estimated the
macro-economic effects of asset purchases and the effects were estimated to have been positive
both for output and inflation. However, a number of central banks also reported side-effects of
APPs that included lower trading volumes of government bonds, price distortions for certain
specific bonds, etc. Spillovers to other countries were also observed in the form of higher capital
inflows leading to an appreciation of exchange rates vis-à-vis US$, significant increase in stock
prices. Disruptive spillovers were also associated with announcement/expectation of reversal of
assets purchase programmes e.g. the ‘taper tantrum’ episode.
Forward Guidance
During the global financial crisis, a few central banks from advanced economies adopted forward
guidance (FG), generally to support accommodative stance and ease monetary policy because
inflation was below the target, and in some cases to address the issue of depressed output growth
and high unemployment rate. According to Potter and Smets (2019), select Central banks
17 reported that FG worked through reducing long term interest rates by inducing expectation of
prevalence of lower policy rates for longer term (and hence lowering uncertainty), thus lowering
term premia. The nature of FG also changed as the situation developed from ad hoc to more
concrete, initially calendar based and subsequently economic conditions based. For example,
ECB provided neither calendar nor outcome based conditions when it introduced FG for policy
rate in July 2013. However, by July 2015, ECB included outcome based guidance, linking policy
actions to expected future path of inflation. Studies suggest that generally FG was effective in
reducing yields. ECB’s FG had largest impact on bonds of intermediate maturities. FG in the US
reduced interest rate uncertainty independent of effects on the expected levels of rates.
Krugman, Dominques and Rogoff (1998) discuss unconventional monetary policy in the context
of problem of deflation which prevents real interest rates to fall for full employment to be
achieved and mentioned the need for central bank to raise inflation expectations to reduce real
interest rates. Reifschneider and Williams (2000) use a modified Taylor Rule and analyse the
deviations in output that were an outcome of the zero bound. They find that the commitment
effect was indeed significant and had an impact on both output and inflation in the US. Fujiki
and Shiratsuka (2002) found a positive impact on output and inflation for Japan. The results are
consistent with similar studies by Fujiwara et al. (2005) and Braun and Waki (2006).
Morgan (2009) looks at the effectiveness of unconventional monetary policy and explores their
importance for emerging markets. He highlights how such policies are instrumental when the
policy rates fall to zero, in the event of a credit crunch or an increase in risk premium which
impairs the monetary policy transmission. It is observed that quantitative easing policies have a
limited impact on bond yields but other kinds of asset purchases (non-government bonds) have
been more successful in relieving market stress such as funding blockages even as such
unconventional policies have had limited impact in stimulating economic growth.
2.5: Monetary Transmission in India
The effectiveness of monetary policy depends on the overall policy environment within which an
economy operates. The liberalisation of financial markets in India since the early 1990s has
proceeded at a gradual pace and has been characterised by "---relaxation of restrictions on entry
18 into banking, creation of new markets for government bonds and other securities and the
reduction of quantitative controls on international capital flows. Banks and other financial
institutions are still subject to mandated holdings of government bonds and large public sector
deficits continue to impact financial markets." Ghate and Kletzer (2016).
Acharya (2020) observed from the evidence that monetary transmission in India has not been
satisfactory in the recent period. As against the policy rate cut of 200 basis points during January
2015 to May 2018, the weighted average term deposit rate (WATDR) declined by 193 basis
points. However, the weighted average lending rate (WALR) on outstanding rupee loans
declined only by 154 base points. Reduction in the WALR on fresh rupee loans was higher at
205 base points as the banks passed on the benefits in the reduction of MCLRs more to the new
borrowers than to the existing borrowers. However, significant transmission occurred only post-
demonetization following the increase in low-cost current and saving account deposits due to
surplus liquidity with the banking system. In the more recent period, in response to the increase
in the policy rate by 50 basis points (from June to December 2018), WALR on fresh rupee loans
increased by 48 basis points, but only 6 basis points on outstanding rupee loans. Also, the
median base rate hardly moved. Since about 24 percent of banks’ loan portfolio is still at the base
rate/ BPLR, this impaired the overall monetary transmission to outstanding rupee loans.
In India, many alternate approaches have been applied to study monetary transmission dynamics.
Swamy (2016) and Acharya (2017) have explored different transmission channels in greater
detail. Ray, Joshi and Saggar (1998), Al-Mashat (2003), RBI (2004), Aleem (2010),
Bhattacharya et al. (2011), Khundrakpam and Das (2011) and Khundrakpam and Jain (2012)
used VAR. New Keynesian model (NKM) to assess transmission sas been estimated by Patra
and Kapur (2012), Goyal (2008) and Anand et al. (2010). Individual equations of the NKM,
mainly concentrated on Philips curve, were estimated by Kapur and Patra (2000), Dua and Gaur
(2009), Paul (2009), Patra and Ray (2010), Mazumdar (2011), and Singh et al. (2011). Mohanty
and Klau (2004), Virmani (2004), Srinivasan et al. (2008), Takeshi and Hamori (2009), Anand et
al. (2010), Hutchison et al (2010), and Singh (2010) have examined Taylor-type rules.
In India, an emerging economy, in addition to effectiveness of different channels, there has been
a growing debate regarding the impact of interest rates on investments, as increasing the growth
19 rate is the prime objective of the Government. The RBI has to support this high growth. This
recent discussion then is focussed on the ability of the RBI to stimulate growth by lowering
interest rates. The underlying assumption under most Taylor-type monetary policy rules has been
that economic growth does respond to monetary stimulus. Therefore, it is intuitive to expect
aggregate demand to react to monetary stimulus through the investment channel. The alternative
view is that lowering interest rates has limited impact, unless capacity utilization is high. This
argument focuses on the underlying economic conditions and argues that a firm’s investment
function depends more on its current capacity utilization and future expectations rather than the
cost of capital or interest rates. The extension of this argument suggests that cyclical downturns
cannot be impacted by interest rates. It is therefore important to investigate the impact of interest
rates on the investment cycle. RBI (2013) concluded, after extensive research, that lower interest
rates do not necessarily support investment and growth.
On the effect of interest rates, Al-Mashat (2003), using a structural vector error correction model
(VECM) for the period 1980:Q1 to 2002:Q4, found that interest rate and exchange rate channels
strengthen the transmission impact of monetary policy while there was little evidence on the
working of bank lending channel due to presence of directed lending under priority sector
lending (Annexure 1). The RBI (1998)) pointed to some evidence of interest rate channel of
monetary transmission. Singh and Kalirajan (2007), using cointegrated VAR approach,
highlighted the significance of interest rate as the major policy variable for conducting monetary
policy in the post-liberalised Indian economy. Pandit and Vashisht (2011) provided evidence that
the policy rate channel of transmission mechanism - a hybrid of the traditional interest rate
channel and credit channel - operated in India and other EMEs (Annexure 1). Mohanty (2012)
showed that there was a co-integrating relationship between monetary policy interest rate
movements with rates across different segments of financial markets (Annexure 1). Furthermore,
lending rates for certain sectors such as housing and automobiles responded relatively faster to
policy changes as compared to other sectors. Interest rate channel accounted for about half of
total impact of monetary shocks on GDP growth and about one-third of total impact on inflation,
indicating the importance of interest rate channel for monetary policy transmission in India.
Kapur and Behera (2012) found that the interest rate channel was effective in the Indian context
and the magnitude of its impact on growth and inflation was comparable to that in major
20 advanced and emerging economies (Annexure 1). Yanamandra (2015) concluded that interest
rate channel was dominant and impacted cost of funds in the economy. Acharya (2017) also
found the interest rate channel to be the strongest in the context of monetary transmission in
India. Goyal and Aggarwal (2017) found that interest rate channel, with repo rate as the policy
rate, is the most effective medium to influence market rates in India (Annexure 1). Sengupta
(2014) found that the interest rate and asset price channels have become stronger and the
exchange rate channel, although weak, shows a mild improvement in the post-LAF period
(Annexure 1).
Pandit and Vashisht (2011) examined the credit channel for India and six other EMEs in a panel
regression framework and found that the policy rate was an important determinant of firms’
demand for bank credit, which confirmed the role of countercyclical monetary policy tool for
setting the pace of economic activity (Annexure 1). Das (2015) found that there is a significant,
albeit slow, pass-through of policy changes to bank interest rates in India (Annexure 1). Banerjee
(2011) examined the direction of credit-output causality for the period 1950-2011 and found
changes in the causality direction over the period: output was predominantly driven by credit in
the pre-1980s period, there was nearly no relationship between the two during the 1980s and
credit was being primarily driven by output in the post-reform period. Swamy (2016) observed
that the bank lending channel remained the principal means of transmission of monetary policy
shocks to the real sector, while asset price or exchange rate channels were not found to be
important in the Indian context.
Mitra and Chattopadhyay (2020) argued that monetary transmission in the recent period was full
and reasonably swift across various money market segments and the private corporate bond
market. However, transmission to bank deposits and lending has been delayed and partial. They
attribute this to rigidity in banks' deposit interest rates. As most of loans are contracted at floating
rates, while most of the deposits are contracted at fixed interest rates, transmission mechanism
tends to get muted. In addition, competitive pressure from mutual funds and small savings
schemes have also impacted transmission mechanism.
21 In a recent paper, Eichengreen, Gupta and Choudhary (2020) studied the transmission from
changes in Repo rate to government bond yields of different maturities (1, 2, 5, 10 years),
treasury bill rates and average lending rates on new and outstanding loans and find that
transmission is greater for treasury bills and bonds of shorter durations and transmission
improved somewhat after adoption of inflation targeting regime (IT). Transmission to bank
lending rates was relatively weak and did not improve with IT. Acharya (2017) and Dua (2020)
also find that transmission, to money market and long term interest rates, is relatively complete
but transmission to bank lending and deposit rates is less complete and slow.
Evidence on the exchange rate channel appears to be mixed. The exchange rate channel is found
to be feeble in India with some evidence of weak exogeneity (Ray, Joshi and Saggar (1998).
Bhattacharya, Patnayak and Shah (2010) found the evidence of incomplete but statistically
significant exchange rate pass through (Annexure 1). While changes in policy interest rates may
influence movements in exchange rates, the level of the exchange rate is not a policy goal, as the
RBI does not target any level or band of the exchange rate but focusses on volatility in exchange
rates. Aleem (2010) pointed out that the exchange rate response to monetary policy shock was
important from the perspective of a proper comprehension of monetary transmission mechanism
in India (Annexure 1). Bhattacharya et al. (2011), based on VECM model, suggested that the
most effective transmission of monetary policy impacting inflation was through the exchange
rate channel. The long-run co-integrating relationship revealed that an increase of 100 bps in the
call money rate had a negligible impact on industrial production (the activity variable) and a
reduction of only 1 bps in inflation; in comparison, one percent currency depreciation increased
inflation by 20 bps. Salunkhe and Patnaik (2017) provide an in-depth analysis of the relationship
between policy rate and inflation (Annexure 1).
On the asset price channel, empirical evidence for India indicates that asset prices, especially
stock prices, react to interest rate changes, but the magnitude of the impact is small. While
interest rates cause changes in stock prices, the reverse causality does not hold. This validates the
point that monetary policy in India does not respond to asset prices, but the asset price channel of
22 monetary policy exists (Singh and Pattanaik, 2012). Further, the wealth effect of increasing
equity prices in stock market has only a short run and small effect on consumption demand in
India (Singh, 2012). It is held that with the increasing use of formal finance (from banks and
non-banks) for acquisition of real estate, the asset price channel of transmission has improved.
However, during periods of high inflation, there is a tendency for households to shift away from
financial savings to other forms of savings such as gold and real estate which are considered to
provide a better hedge against inflation. To the extent that these are funded from informal
sources, they may respond less to contractionary monetary policy, thus weakening asset price
channel in India.
Khundrakpam and Jain (2012), using SVAR examine relative importance of various channels
and conclude that interest rate channel, credit channel and asset price channel are important
while exchange rate channel is weak (Annexure 1).
There are significant monetary policy transmission lags which have been observed by several
authors. RBI (2005) using a VAR framework for the period 1994-95 to 2003-04 found that
monetary tightening through a positive shock to the Bank Rate had the expected negative effect
on output and prices with the peak effect occurring after around six months. Anand et al. (2010)
employed a DSGE model framework and their results indicated that the peak effect of a 100 bps
increase in the nominal policy rate (call rate) was 35-45 bps on output and around 15 bps on
inflation and the peak effect on both output and inflation was felt in the first quarter after the
policy rate shock. Patra and Kapur (2010) found that aggregate demand responded to interest rate
changes with a lag of at least three quarters. However, the impact of monetary policy could
persist up to two years (Annexure 1). Mohanty (2012), using a quarterly structural VAR model,
found that the peak effect on output growth was observed with a lag of two quarters and that on
inflation with a lag of three quarters while the overall impact persisted through 8-10 quarters.
Mishra (2016), however, observed monetary easing through a positive shock to broad money had
a positive effect on output and prices with peak effect occurring after about two years and one
year, respectively. Further, exchange rate depreciation led to increase in prices with the peak
effect after six months.
2.6: Conclusion
23 The review of literature, globally and domestically, reveals that interest rate channel is most
significant amongst four different channels. The choice of techniques, as well as variables, have
varied in different countries and for different time periods. The empirical literature has also
considered call money rates, in addition to the policy rate or the Repo rate while estimating the
transmission mechanism.
24 Section 3
Evolution of Monetary Policy Operating Framework in India
Globally, in most countries, monetary policy framework has evolved in response to and in
consequence of financial developments, openness and shifts in the underlying transmission
mechanism. The issue became important after the global financial crisis in 2008 when the focus
of the economists was drawn to financial stability, in addition to price stability, the traditional
objective of the central bank. In India, in 1997, after the Asian Crisis, the RBI had followed the
Multiple Indicator Approach (MIA), which had macroeconomic and financial indicators, and one
of which was inflation. The purpose of adopting MIA was to factor economic-wide
considerations, ranging from fiscal to financial sector, while fixing the policy interest rate. In this
brief section, evolution of the monetary policy in India is discussed.
3.1: Evolvement of Policy Objectives
The evolution of the monetary policy framework in India can be seen in various phases and has
been following the developments taking place in the financial system and the changing nature of
the economy (Mohanty, 2012; Das, 2020). The recent developments in the supervision and
regulation of the financial institutions and the growing importance of the nonbanking financial
intermediaries has renewed the focus to revise the framework. The focus remains on promoting
seamless real-time transactions with anchored expectations of the public and improving the
credibility of policy in ensuring price stability with growth and a resilient financial system in
place.
The Reserve Bank of India was established in 1935. During the formative years (1935-1950), the
focus of monetary policy was to regulate the supply of and demand for credit in the economy
through the Bank Rate, reserve requirements and open market operations (Deshmukh, 1948).
During the development phase (1951–1970), monetary policy was geared towards supporting
plan financing, which led to introduction of several quantitative control measures to contain the
consequent inflationary pressures (Bhattacharya, 1966). While ensuring credit to preferred
sectors, the Bank Rate was often used as a monetary policy instrument. During 1971–90, the
focus of monetary policy was on credit planning as 20 banks had been nationalized, pursuing
25 social objectives (Narasimham, 1977). Both the statutory liquidity ratio (SLR) and the cash
reserve ratio (CRR) prescribed for banks were used to balance government financing and
inflationary pressures. The 1980s saw the formal adoption of monetary targeting framework
based on the recommendations of the RBI (1985). Under this framework, reserve money was
used as the operating target and broad money (M3) as an intermediate target. Thus, the monetary
policy was dynamically responding to the evolvement of the economic factors in the economy
(Malhotra, 1985). Subsequently, structural reforms and financial liberalisation in the 1990s led
to a shift in the financing paradigm for the government and commercial sectors with increasingly
market-determined interest rates and exchange rates.
In the 1990s, as the efficacy of the monetary targeting framework got undermined with
liberalization and financial innovations, the need to revise the existing framework emerged
(Rangarajan, 1997). In April 1998, the Reserve Bank of India formally adopted the multiple
indicators approach. In this approach, in addition to monetary aggregates, indicators like credit,
inflation, output, exchange rate, trade flows, market returns, and fiscal performance were used to
formulate policy. With increasing market orientation, the deregulation of interest rates enabled
the shift from direct instruments towards indirect instruments of monetary policy. Short term
interest rates became instruments to signal monetary policy stance of RBI. To ensure stable short
term interest rates, the emphasis was laid on integrating the money market with other segments
of the financial market.
In the period following the Global Financial Crisis (GFC) in 2008-09, the credibility of Multiple
indicator approach was questioned for not providing a clearly defined nominal anchor. In 2014
based on the recommendations of the Committee on Monetary Policy Framework (Chairman:
Urjit Patel; RBI (2014)), it was recommended that inflation should be the nominal anchor for the
monetary policy framework. The Government of India (GoI) and RBI on February 20, 2015,
signed the Monetary Policy Framework Agreement (MPFA), adopting flexible inflation targeting
formally with the amendment of the RBI Act 2016. The new objective restates maintaining price
stability as the primary objective while observing the objective of growth. The numerical target
of 4 percent for CPI headline inflation has a tolerance band of +/-2 percent. The relative
emphasis on growth and inflation depends on the emerging developments in the economy.
26 The liquidity management operations of the RBI were able to move away from direct
instruments to indirect market-based instruments. Beginning in April 1999, the RBI introduced
liquidity adjustment facility (LAF) to manage liquidity through Repo (repurchase Agreements,
liquidity injection) and reverse Repo (liquidity absorption) operations. From 2003 till May 2,
2011, monetary policy signals were provided through changes in both Repo and reverse Repo
rates in conjunction with variations in the cash reserve ratio. During episodes of excess liquidity
(2001 through 2006 and again from 2008:Q4 to 2010:Q2), the reverse repo rate was the effective
policy rate. On the other hand, during episodes of monetary tightening/liquidity shortage
(2007:Q1 to 2008:Q3 and 2010:Q3 to 2011:Q4), the repo rate became the effective policy rate.
Thus, the policy rate, during the post-2003 period, switched between Repo and Reverse Repo
rates. While this helped to develop interest rate as an important instrument of monetary
transmission, this framework witnessed certain limitations due to the lack of a single policy rate
and the absence of a firm corridor. In this context, the RBI introduced a new operating procedure
in May 2011 where the weighted average overnight call money rate was explicitly recognised as
the operating target of monetary policy and the Repo rate was made the only one independently
varying policy rate to transmit policy signals more transparently.
3.2: Improving Transmission Mechanism
Along with the evolution of monetary policy operating framework, there has also been a gradual
move towards improving the effectiveness of monetary policy transmission to bank lending
rates. The focus on developing the financial sector was at the core of reforms undertaken since
1991 (Singh, 2005). In this context, to help develop financial markets, market determined
interest rates through auctions were introduced in the government securities market, primary and
secondary dealerships were set up, new financial instruments were conceived and experimented,
and in general, liberalisation of the markets was initiated. To ensure that the monetary policy is
independent, the system of automatic monetisation of deficit through ad hoc Treasury Bills was
stopped in 1997 by an agreement between the RBI and the Central Government. To take care of
the fiscal requirements, short term financing of the central and State Governments, through ways
and means advances was modernised. To ensure an adequate supply of instruments with
appropriate maturity, the maturity period of government securities was modulated, considering
the requirements of insurance, provident and pension funds. To ensure that the banking system is
27 robust and competent, macroprudential norms and early warning signals were devised for
financial institutions by mid-2000s. The regulatory and supervisory mechanism of the banking
system, mainly commercial banks, was strengthened. The RBI was liberal in granting licenses to
private and foreign banks to operate in the country. The licensing scheme for new types of banks
was also initiated under which small and payment banks were operationalised. The consolidation
exercise of public sector banks was also successfully completed in recent years. The
development finance institutions like IDBI, ICICI and HDFC were discontinued and merged
with commercial banks. India also became an active member in evolving Basel norms and
meeting the requirements stipulated by Bank for International Settlement.
The RBI also made efforts to strengthen the regulatory and supervisory function in the case of
urban and state cooperative banks, and non-banking finance companies. The country witnessed
the growth of self-help groups and microfinance institutions with the active support of National
Bank for Agriculture and Rural Development since mid-1980s.
As the markets developed and integration improved, the expectation of the RBI was that
transmission should also be more effective. Acharya (2020) and RBI (2017) discuss the
evolvement of benchmarking rates and the efforts made by the RBI to improve transmission of
the monetary policy through the banking channel. In 1994, the RBI introduced the concept of
prime lending rate (PLR). To introduce transparency, in 2003, the banks were advised to fix
benchmark PLR (BPLR) and provided the freedom to lend below BPLR. Since then, the RBI has
changed the system from BPLR to base rate in 2010, to marginal cost based lending rate in 2016
and external benchmark rate in 2019 (Table – 3.1).
However, as can be observed, the benchmarking was mainly on the lending operations of the
commercial banking sector and urban cooperative banks. The NBFCs including housing finance
companies, SHGs, and MFIs followed their independent pattern, based on the cost of
borrowings. These institutions borrowed at different rates from different sources and their
lending rates were not related to the RBI’s policy rate. The flow of credit from these sources is
nearly one-third of the total credit flow in the economy or almost half from the commercial
banking sector, is substantial, and impacts the transmission to the real sectors of the economy.
28 Table – 3.1: Evolution of Lending Rate System in India
Year Lending rateIntroduction
1994Prime Lending
Rate (PLR)
The PLR regime was introduced in 1994. However, both PLR and spread over PLR
were seen to vary widely across banks/bank groups. Moreover, the PLRs continued to
be rigid and inflexible in relation to the overall direction of interest rates in the
economy.
2003 Benchmark
Prime Lending
Rate (BPLR)
With the aim of introducing transparency and ensuring appropriate pricing of loans—
wherein the PLRs truly reflected the actual costs—the PLR was converted into a
reference benchmark rate and banks were advised in 2003 to introduce the BPLR
system. Under this system, banks were given the freedom to lend below the BPLR.
While lending below the BPLR was expected only to be at the margin, it was observed
that about 77 percent of banks’ loan portfolio was at sub-BPLR. This affected the
transmission of monetary policy instruments. Given these limitations, the PLR and
BPLR systems did not lead to monetary transmission to the real economy to the
desired extent.
2010 Base Rate In July 2010, the BPLR system was replaced with the base rate system and banks were
asked to calculate bank-specific base rate based on an indicative formula prescribed by
the Reserve Bank and the spread over the Base Rate. Banks were allowed flexibility in
the determination of cost of funds; they could use average, marginal or blended cost
for base rate calculation. This flexibility, however, resulted in opacity in the
computation of base rate. This was seen when the average cost of funds was used
which remained somewhat rigid due to the term nature of fixed-rate deposits. The
change in the spread over the base rate over time was not uniform across borrowers.
2016 Marginal Cost
Based Lending
Rate (MCLR)
In April 2016, Marginal Cost-Based Lending Rate system was introduced for banks
which were linked to the marginal funding cost of each bank based on the prescribed
formula for its computation, even as it provided for some discretion to banks.
However, even under the MCLR system, the transmission to the existing borrowers
has remained muted as adjustments to the MCLR and/or spread over MCLR by banks
were done in many cases in an arbitrary manner. This was evident from the fact that
overall lending rates were kept high in spite of monetary policy being accommodative
from January 2015 to May 2018.
2019 External
Benchmark Rate
From April 1, 2019, floating rate loans (personal or retail loans, loans to micro and
small enterprises, and any other category of loans at the bank’s discretion) extended by
banks have been linked to either the policy repo rate or a market benchmark rate
(three-month or six-month T-bills or any other rate produced by Financial Benchmark
India Private Limited [FBIL]). The spread over the benchmark rate would remain
unchanged unless the borrower’s credit assessment undergoes a substantial change and
as agreed upon in the loan contract.
Source: Acharya (2020) and RBI (2017).
29 3.3: Conclusion
The monetary policy, as well as objectives, have evolved over the years, globally and domestically. The RBI
has been examining the issue of transmission and taking initiatives to make the transmission more complete.
The RBI made extensive efforts since 1994 to develop the markets initially which have become more integrated
in recent years. The RBI also made efforts to benchmark the lending rates so that the policy rate is effectively
reflected in the banking operations. Hence the expectations by the RBI that transmission of the monetary policy
will be more swift. However, the lending rate of the credit offtake from NBFCs, SHGs and MFIs, which
constitute about one-third of total lending, are yet not aligned with the RBIs policy rate.
30 Section 4: Methodology of the Study
The data used in this study has been extracted from the Reserve Bank of India, Government of India - Ministry
of Statistics and Program Implementation (MoSPI), Ministry of Labour and Employment (MoL&E) and
Ministry of Finance (MoF) for empirical investigation.
3.1 Variables and Data Sources
The study used quarterly data from the first quarter (Q1) of 1998 to the fourth quarter (Q4) of 2018-19. The
quarterly data pertain to the variables such as Gross Domestic Product , Inflation, Money Supply, Repo Rate,
Index of Industrial Production, Government Final Consumption Expenditure, Private Final Consumption
Expenditure, Gross Capital Formation, Exports, Imports, Exchange Rate, BSE-Sensex, NSE-Nifty, Public
Investment, Private corporate Investment and Household Investment as macroeconomic variables to understand
different channel of monetary transmission in India. The details of the computation of the data are presented in
Annexure 2.
Adjusted Real GDP is computed by splicing GDP at constant price of 1999-2000, 2004-05 and 2011-12 data at
2011-12 prices and then adjusted with error so that sum of four Spliced Quarterly Real GDP (2011-12) is equal
to Annual Real GDP (2011-12). IIP growth rate is computed by splicing Index of Industrial Production at
2011-12 base and then growth of index. Repo rate is the quarterly arithmetic average. Real Effective Exchange
Rate (REER) and Nominal Effective Exchange Rate (NEER) rate at trade based weight is computed by splicing
both index at 2004-05 base. WPI inflation rate is computed by splicing Index of WPI at 2011-12 base and then
growth of index. CPI inflation is computed by taking CPI-IW and CPI- Combined (Urban + Rural), First CPI-
IW is taken from April, 1998 to December 2009 then from January, 2010 CPI- Combined is taken, after that
both indexes spliced at 2011-12 base, finally taken growth of quarterly index to get CPI inflation. It is observed
that the CPI-Combined has a strong and statistically significant correlation with the CPI-IW so CPI- IW can be
used before 2010 (RBI, 2014).
31 Prime Lending Rate is Quarterly arithmetic average of Prime Lending Rate (PLR) from April, 1998 to March,
2003, Benchmark Prime Lending Rate (BPLR) from April, 2003 to June, 2010, Base Rate from July, 2010 to
March, 2016, and Marginal Cost of fund based Lending Rate (MCLR) from April, 2016 to March, 2019.
Quarterly Private Corporate Investment (percentage of GDP) is computed by taking an individual share of
Private Corporate Investment in Total Gross Capital Formation from Annual Private Corporate Investment data
and then multiplied this share with Quarterly Total Gross Capital Formation data (at current price). Finally
computed ratio of Private Corporate Investment to Quarterly GDP (at current price). Quarterly Public
Investment (% of GDP) is also computed by the same way as Quarterly Private Corporate Investment is
computed. Nominal Exchange rate is exchange rate of INR in terms of USD.
Index of National Stock Exchange (NSE), Non Food Credit (NFC), Total Deposit, Prime Lending Rate (PLR),
G-Sec/Treasury Bill Yields, Weighted Average Call Money Rate, Commercial Paper interest rate, Certificate of
Deposit Interest Rate and 5 Year AAA Rating Corporate Bond yield are quarterly arithmetic average. The
details of the particular sources of the data are presented in Annexure 2. The data used for analysis is in Table
4.1 and the variable key is presented in Table 4.2.
32 Table 4.1: Data Used for Analysis
S.
No.
Name of the variable Unit of MeasurementData Source
1
Gross Domestic Product
(GDP)
Spliced adjusted level at Constant
2011-12 Prices (in Crore)
National Accounts Statistics
(NAS)
2IIP
Spliced Growth Rate (Base: 2011-12
= 100)
CSO
3Repo Rate
Average of Quarter Starting from
Apr-June
RBI
4
Real Effective Exchange Rate
(REER)
Spliced Index Number, (Base: 2004-
05 = 100) at Trade Based Weight
RBI
5
Nominal Effective Exchange
Rate (NEER)
Spliced Index Number, (Base: 2004-
05 = 100) at Trade Based Weight
RBI
6Exchange Rate (INR/USD) In INR/USDRBI
7
National Stock Exchange
(NSE)
Quarterly Average Index at Base:
1995=1000
RBI
8Non-Food Credit (NFC) Quarterly Average in Crore RBI
9Total DepositQuarterly Average in Crore RBI
10Prime Lending Rate In Percent RBI and Commercial Bank
11CPI
Spliced Growth Rate Based on (Base:
2011-12=100).
RBI
From 2010 January CPI-combined
and prior to that CPI-IW
12WPI
Spliced Growth Rate Based on (Base:
2011-12 = 100).
RBI
13G-Sec/Treasury Bill Yields
Quarterly Average- 91 Day, 364 Day,
5 Year G-Sec, 10Year G-Sec
EPW Research Foundation
14
Weighted Average Call
Money Rate
Quarterly AverageRBI
15Commercial Paper
Quarterly Average of Lower Rate of
Interest
EPW Research Foundation
16Certificates of Deposit
Quarterly Average of Lower Rate of
Interest
EPW Research Foundation
17
5 Year AAA Rating Corporate
Bond
Quarterly Average Yield
Fixed Income Money Market
and Derivatives Association Of
India
Note- Quarter is starting from Apr- Jun.
All the growth rate is taken from corresponding previous quarter
Table 4.2: Variable Key
33 VariablesSymbol
1 Repo Rate REPO
2 NSE IndexNSE
3 BSE IndexBSE
4 Private Corporate Investment (as % of GDP) PCI
5 91 Days- 6 months Deposit RateDR91
6 1-2 years Deposit RatesDR2Y
7 Lending Rates -Prime Lending RatePLR
8 91 days - G-Sec RatesT91/TBR91
9 364 days G-Sec RatesT364/TBR364
10 5 Year G-Sec Rates 5GSEC
11 10 Year G-Secs10GSEC
12 Weighted Average Call Money RateWACR
13 Lower CP rate LCP
14 Lower CD rate LCD
15 Bond Market AAA rated 5YCB
16 Consumer Price Index- InflationCPI/INFCPI
17 Wholesale Price index-InflationWPI/INFWPI
18 Exchange Rate - ln transformed ER
19 Nominal Effective Exchange RateNEER
20 Log Transformed NEERLnNeer
21 Real GDP (in crores)RGDP
22 Real GDP - growth rateZRGDP/ RGDGR/GDPGR/GZGDP
23 IIP-growth rate ZIIP
24 Log Non-Food Credit -growth LNNFC
25 Non-Food Credit – growthNFC/GNFC
26 Total Deposits - growth ZTD
27 Total Deposits (crores)TDR
Notes: 1) Z suffix denotes growth rates, and
2) Ln suffix and Log denote Logarithmic transformation
3.2 Empirical Investigation Methodology
The methodology followed is standard in the empirical analysis. The stationarity of the variables is examined
since regressing on non-stationary
2
time series can lead to spurious regression outcomes. The tests for
2 Time-series with mean and autocovariances independent
34 identifying unit root in individual time series are Augmented Dickey-Fuller (1979) test with Akaike Information
criteria (AIC) and Schwarz information Criterion (SC), and Phillips-Perron(1986) test.
3
Consider a simple AR (1) process:
yt = ρyt-1 + x’tꝺ + ɛt, - (1)
where xt are optional exogenous regressors which may contain a constant, or a constant and trend, ρ and
ꝺ
are
the parameters to be estimated, and the ɛt, are assumed to be white noise. If the modulus of |ρ|≥ 1, y is a (trend-)
stationary series. The unit root tests that we perform have the null hypothesis H0: ρ = 1 against the one-sided
alternative H1: ρ <1. In some cases, the null is tested against a point alternative.
The Augmented Dickey-Fuller (ADF) test is performed by subtracting yt-1 from both sides of the equation:
Δyt = αyt – 1 + x’tꝺ + ɛt, - (2)
where α = ρ -1. The null and alternative hypothesis may be written as,
H0 : α =0
H1 : α < 0
and evaluated using the conventional t-ratio for α:
tα ¿^α/(se(^α))
where ^α is the estimate of α, and se(^α)¿ is the standard coefficient error.
Phillips-Perron
4
tests assess the null hypothesis of a unit root in a univariate time series y. All tests use the
model:
yt = c + δt + a yt – 1 + e(t).
The null hypothesis restricts a = 1. Variants of the test, appropriate for series with different growth
characteristics, specify the drift and deterministic trend coefficients, c and δ, respectively, to be 0. The tests use
modified Dickey-Fuller statistics to account for serial correlations in the innovations process e(t).
After performing the unit root tests, the next step is to select the optimal lag. For lag order selection various
criterion are used like, likelihood-ratio test statistic (LR), Akaike Information Criteria (AIC), Final prediction
error (FPE), Schwarz Information Criteria (SC) and Hannan-Quinn Information criteria (HQ) test under the
environment of Vector Auto Regression(VAR).
3 at 5% level of significance.
4 Phillips, Peter & Perron, Pierre. (1986).
35 Finally, in order to see the policy response, the study uses a Structural Vector Auto Regressive (SVAR)
framework with external variables as exogenous variables to control for external influences. Sim’s vector auto-
regression (VAR) methodology has been extensively used in examining the efficacy of monetary policy
transmission across several countries. According to Sims et al., (1990), the VAR approach is constructed to
identify the relation of the variables instead of parametric estimation. This approach provides a major advantage
of taking into account the simultaneity between monetary policy instruments and relevant macroeconomic
variables. However, there are several versions of VAR models to examine monetary policy transmissions such
as the traditional VAR, Structural VAR (SVAR) and Factor Augmented VAR (FAVAR). SVAR models, unlike
the traditional VAR models, provide explicit behavioural interpretations for all the parameters. The main
purpose of structural VAR (SVAR) estimation is to obtain non-recursive orthogonalization of the error terms
for impulse response analysis. This alternative to the recursive Cholesky orthogonalization requires the user to
impose enough restrictions to identify the orthogonal (structural) components of the error terms. Following
Bernanke and Blinder (1992), we use a standard SVAR approach to examine how monetary policy shocks
affect the real economy. The SVAR model has been preferred as it enables providing explicit behavioral
interpretations of the parameters.
SVAR is a multivariate, linear representation of a vector of observables on its lags and (possibly) other
variables as a trend or a constant. The interpretations of SVAR models require additional identifying
assumptions that must be motivated based on institutional knowledge, economic theory, or other extraneous
constraints on the model responses. Only after decomposing forecast errors into structural shocks that are
mutually uncorrelated and have an economic interpretation, one assesses the causal effects of these shocks on
the model variables. These exogenous variables are assumed to have both contemporaneous and lag impact on
the endogenous variables without any feedback effect. Further, in view of the limited number of variables which
can be considered in the SVAR without losing degrees of freedom, each of the channels of transmission is
examined only one at a time. This involves estimating a baseline SVAR model, which is augmented by the
variables representing a particular channel of transmission each time separately. It will isolate purely
exogenous, purely independent movements or shocks to variable of interest and see how macroeconomic
variables react to it i.e., via the impulse response. The structural model isolates purely exogenous shocks and
gets the responses of the endogenous variables after the economy is hit by these shocks. Uncovering the
structural model is called identification. This is identified as follows:
36 A structural model of the form where Xt depends on its lag and structural shocks ut assuming that the structural
shocks are independent among themselves.
AX
t
=β
0
+β
1
X
t−1
+u
t
Or in general form it is given as,
AX
t
=∑
i=1
n
β
1
X
t−1
+u
t
,u
t
N(0,D)
where X
t is a (N ×1) vector of the endogenous variables and β
1 is a (N ×N) matrix containing the parameters on
the i
th
lag, with A representing the contemporaneous interactions between the variables. The (N × 1) vector of
disturbances,u
t represents the structural shocks and has covariance matrix D, which is a diagonal matrix
containing the variances. It is the fact that the covariances of u
t are all zero that gives ut its structural
interpretation, since each shock is, by definition, unique.
In the first phase of empirical analysis in order to understand the effect of policy rate on various sectors the
study has employed SVAR approach. In the following SVAR model shocks has been provided in form of repo
rate, 91 Treasury bill rate and weighted call money rate to NSE, GDP, WPI, 5YGSEC and 5YCB.
In matrix form, it can be expressed as:
[
1a
12a
13a
14a
15
a
211a
23a
24a
25
a
31a
321a
34a
35
a
41a
420a
44a
45
a
51a
52a
53a
541][
Y
t
X
t
Z
t
P
t
T
t]
=
[β
10
¿][β
20
¿][β
30
¿][β
40
¿]¿
¿
¿¿
[
β
11β
12β
13β
14β
15
β
21β
22β
23β
24β
25
β
31β
32β
33β
34β
35
β
41β
42β
43β
44β
45
β
51β
52β
53β
54β
55][
Y
t−1
X
t−1
Z
t−1
P
t−1
T
t−1]
+ [
u
Y
u
X
u
Z
u
P
u
T]
Multiplying the VAR by A
-1
we get the reduced form VAR i.e. given as:
A
−1
AX
t=A
−1
β
0+A
−1
β
1X
t−1+A
−1
u
t
Or X
t
=G
0
+G
1
X
t−1
+e
t , i.e. the reduced form, given A
−1
A=I
Here I is the identity matrix. Matrix A also relates to structural shocks u and forecast errors e
t:
e
t=A
−1
u
t
37 Forecast errors e is a linear combination of the structural shocks u. Being a theoretical construct, it is non-
observable. As Sims (1986) highlighted, it is an interpretation of historical data. What we have at hand is the
evolution of the key financial system variables. While estimating, we run regressions of each variable against its
past and the past of other variables in the system. The study will get the structural model of the form:
AX
t
=β
0
+β
1
X
t−1
+u
t
This isolates the exogenous shocks and measures the impact of these shocks on the variables included in the
model. Given the objective of the current study, we have imposed restrictions on the contemporaneous
relationship of endogenous variables and also on the old matrix A. As we had,
e
t=A
−1
u
t,
To show the relationship between forecast errors and structural shocks, Bernanke and Mihov (1998), Blanchard
and Perotti (2002) use a more general way of relating the errors and shocks in SVARs:
Ae
t
=Bu
t
,
Where, specification of these equations can have both errors and shocks on the right hand side. To get the
system responses to shocks one needs to have;
e
t=A
−1
u
t or e
t
=Fu
t , where F=A
−1
B
For this, the study uses a modified version of Kim and Roubini’s (2000) non-recursive identifying restrictions
on the contemporaneous coefficients taking into account key macroeconomic variables. The standardized
structural shocks comprise of shocks on monetary policy rate, the capital markets, the banking sector, the real
sector output and the exchange rate. The contemporaneous matrix A with restrictions is specified by matrix
patterns and/or text expressions. Pattern matrices are a convenient way to place simple and constant constraints
on the individual elements of a structural matrix, whereas on the other side, the text expressions provide the full
range of allowed constraints. Here the study takes into account the short run representation, given the fact that
policy targets are pursued with short to medium term horizon.
The short run restriction on REPO, NSE, GDP, WPI, 5YGSEC and 5YCB can be defined as
38 A=
[
1C(5)C(9)C(10)C(12)C(15)
0 1 0 0C(13)C(16)
C(1)C(6)1C(11)0 0
C(2)0 0 1 0 0
C(3)C(7)0 0 1C(17)
C(4)C(8)0 0C(14)1]
In the above model, the study will put restrictions on various parameters when it will change its policy
instrument (i.e., Repo Rate, Call Money Rate & 91 Days Tresurery Bills ) for testing different monetary policy
transmission channels in India.
39 Section 5
Trend Analysis and Quantitative Results
The monetary policy, along with objectives and instruments, has evolved in recent years, both globally and
domestically. The global financial crisis exposed the risk of having the monetary policy focus exclusively on
single objective of inflation. The meltdown in the financial system alerted the policy makers that monetary
policy should also be accountable for the banking system and financial sector system through which the
monetary policy operates.
In the previous sections, the discussion has focussed on the review of literature and various channels of
monetary transmission, and evolution of monetary policy in India and the Methodology of the Study. In this
section, trend analysis of data related to monetary policy and different channels of monetary transmission is
presented to evaluate the relationship between different variables. Then quantitative results are presented.
5.1: Repo Rate and Macro variables
The plot of growth rate in real GDP and Repo exhibits a mixed trend over the period of analysis (Fig 5.1). In
recent period, it is noteworthy that real GDP growth has increased following a decline in repo rate in most
instances. In 2003 and 2009, there has been monetary tightening leading to slower growth. The monetary easing
has been continuous since 2011. From 2015-18, significant transmission of monetary policy occurred post-
demonetization following the increase in low-cost current and saving account deposits due to surplus liquidity
with the banking system.
Figure 5.1: - Time series plots of Select Macroeconomic Variables
O bject 53
40 There has been a consistent depreciation of the Indian rupee in relation to the US Dollar, mainly due to interest
rate and inflation differential. The post-2008 rush for US dollars is also apparent from Fig - 5.2. However, a
modest improvement in our exchange rate came with the backdrop of high repo rates in the early 2000s. The
RBI, as other central banks, intervene in the foreign exchange market to contain volatility. An inverse
relationship between Repo rates and private corporate investment is noted in most years, with a clear trend n
2000, 2004, 2008 and 2009 (Fig - 5.3).
Figure 5.2Figure 5.3
O bject 55O bject 57
The deposit rates are more closely related to the Repo rate while the prime lending rate, factoring the risk
premia follows the trend in recent years (Fig - 5.4). There is a broad co-movement of the three series or similar
pattern over the time period under consideration. A consistent decline in the growth rate of non food credit
growth can be observed from the highs of 2004 (Fig 5.5).
41 Figure 5.4 Figure 5.5
O bject 59 O bject 61
The asset prices, in terms of BSE and NSE have consistently increased suggesting a strong time trend given the
reforms and growth in the economy (Fig - 5.6).
Figure 5.6
O bject 63
The relationship between price variables and Repo rate is consistent with the fact that RBI increases policy rate
during periods of high inflation (Fig 5.7). During 2014, inflation based on the consumer price index was high
because of higher food prices due to 2014 agricultural drought. Inflation based on the wholesale price index
slowed, mainly on account of lower fuel prices. Repo rate, weighted average call rate, commercial paper rate
and certificate of deposit rate tend to co-move during the period under study (Fig 5.8).
42 Figure 5.7 Figure 5.8
O bject 65O bject 67
The co-movement of the yields on government paper – 91-day Treasury Bills, 5-year G-Sec and 10-year G-Sec
Yield and Repo Rate, from 2001. The figure shows that Repo has broadly remained within the range provided
by 91-day Treasury bills (Fig 5.9). The trend in 5 year AAA corporate bond yield is similar to that of G-Secs.
While the graph shows that the variables tend to co-move, however, it is noteworthy to observe the narrowing
risk premiums on corporate bonds over the years which is reflected in the reduction in yields in 1998Q1 to
2018-19Q1 (Fig 5.10).
Figure 5.9 Figure 5.10
O bject 69O bject 72
43 5.2: Growth, Prices and Investment
The relationship between growth and inflation is presented in Fig - 5.11. results are consistent with the fact that
as inflation increases with the rise in economic activity accelerates GDP growth rate. The high growth period of
2003-2008 coincided with low inflation. However, towards the latter part of the period as inflationary pressures
rose it warranted monetary tightening. From 2008-10, reflecting the impact of global financial crisis, growth
decelerated and weak commodity prices and relatively stable exchange rate contained inflation. That created the
space for monetary easing. .
The relationship between growth rate of real GDP and private corporate investment is presented in Fig - 5.12.
The figure shows that increase in private corporate investment growth rate has a positive impact on GDP
growth rate, which is consistent with economic theory. Investment is a component of aggregate demand (AD).
Therefore, if there is an increase in investment, it will help to boost AD and economic growth.
Figure 5.11
O bject 75
From 2010-12, India recovered ahead of the global economy, and actual growth in 2010–11 at 9.3 percent
exceeded the expectations and our potential growth rate. With a sharp recovery in growth, inflation too caught
up rapidly, partly complicated by a rebound in commodity prices (Fig - 5.11). From 2012-14, softening of
inflation created space for monetary easing. However, growth is yet to pick up reflecting both weak global
demand, domestic supply constraints and slowdown in corporate investment. At the macroeconomic level
supply bottlenecks and sluggish demand can depress Marginal Efficiency of Capital, which can more than offset
the beneficial impact of a lower lending rate on investment and growth.
44 Figure 5.12
O bject 77
5.3: Correlation Analysis
The selection of variables for modeling exercise is based on undertaking a comprehensive analysis in addition
to the time-series plots illustrated earlier. Further, cross correlation matrix was examined (Table 5.1, detailed
correlation statistics in Annexure 3). The correlation coefficients of the Repo rate with other macroeconomic
variables for four distinct time periods reveals mixed results. Finally, after testing for unit roots (Annexure 4),
pair-wise granger causality tests for individual variables were also estimated (Annexure 5). However, it needs to
be recognized that there are limitations of statistical exercise such as that of establishing causality between two
variables at a point in time (as is estimated by Granger causality) that are often influenced by numerous
exogenous and endogenous shocks that operate on dynamic basis. Therefore, based on macroeconomic intuition
some variables that may not show a statistical causality, but are known from theory to have a causal relationship
have also been considered. Illustratively, though statistically, Repo Rate and WACR, CPI, NFC do not show
causality but has been considered in the study.
45 Table 5.1: Correlation Coefficient of Repo Rate with other Macroeconomic variables
Repo Rate with
1998-2002 2003-2007 2008-2012 2013-2018
REPO 1.00 1.00 1.001.00
ZRGDP -0.19 0.05 -0.07 0.29
WPI -0.18 -0.08 0.490.10
CPI 0.12 0.36 -0.35 0.63
PCI -0.27 0.34 -0.29 0.36
NEER -0.37 0.07 -0.45 -0.47
BSE -0.61 0.66 0.41 -0.72
NSE -0.59 0.67 0.30 -0.70
PLR -0.14 0.86 -0.40 0.90
DR2Y -0.25 0.72 0.500.94
WACR -0.53 0.53 0.950.93
5GSEC -0.51 0.52 0.880.85
5YCB -0.54 0.49 0.880.86
T91 -0.60 0.66 0.970.92
NFC -0.07 -0.33 0.260.48
5.4: Analyzing the dynamic response of instrument specific variables
5

After testing for the unit-roots (Annexure 4) to examine the time series characteristics of the variables in the
analysis, the dynamic response of specific variables to a shock to Repo rate is investigated, i.e., NSE (asset
prices), ZRGDP (Real Sector Output), WPI (Price variable), and 5GSEC and 5YCB (markets).
These variables have been selected based on the signaling properties, i.e., ZRGDP, NSE, 5GSEC, 5YCB and
WPI. The variable GDP is for real output reflecting the wealth creation ability and overheating risk. For the
capital markets, the logarithmic series of National Stock Exchange index (NSE) has been considered, which
indicates the liquidity disruptions that may be a materialization of the market's ability to allocate surplus funds
to investment opportunities within the economy efficiently. Five-year G-Sec Yields (5GSEC) along with high-
quality, triple A rated market corporate bond rates (5YCB) have been considered as a proxy for understanding
the investment sentiment in the economy. Finally, Wholesale price index (WPI) has been considered as a
5 We would like to thank Dr. Pabitra Kumar Jena, School of Economics, Shri Mata Vaishno Devi University, Katra for analysis in this
sub-section.
46 measure of inflation or price stability.
6
The restrictions imposed on Structuarl VAR are presented in Annexure
6.
Further, the SVAR is used to understand the interest rate transmission using the Repo Rate (REPO), 91-day
Treasury bills rate (T91), and Weighted Average Call Money Rate (WACR) as alternative policy variables.
Repo rate is the official policy rate used in RBIs' monetary policy. Repo appears as the principal transmission
instrument of monetary policy in India (Mohan, 2004). Further, it had been observed by Taylor and Williams,
2010) that the Repo rate worked efficiently in transmitting the monetary policy signal. The weighted average
call money rate (WACR), under the operational objective of liquidity management, is the key variable. As per
the policy mandate, it should be reverting towards the repo rate over time (Patra & Kapur, 2016), sharing an
equilibrium relationship with the repo rate in the long run. The 91-day Treasury bills rate was considered a
proxy for a policy interest rate as WACR is more volatile than the 91-day Treasury rates (Kumawat and
Bhanumurthy, 2016).
Table 5.2: Composite SVAR Matrix
NSE ZRGDP WPI 5YCB 5GSEC
REPO0.93*
(2.34)
[0.01]
0.71*
(10.34)
[0.00]
-0.06
(-0.70)
[0.48]
0.86*
(3.56)
[0.00]
0.82*
(3.50)
[0.00]
T911.65*
(4.75)
[0.00]
0.50*
(11.32)
[0.00]
-0.02
(-0.42)
[0.66]
1.33*
(8.93)
[0.00]
1.32*
(8.68)
[0.00]
WACR1.94*
(3.94)
[0.00]
0.87*
(11.35)
[0.00]
-0.06
(-0.57)
[0.56]
1.58*
(5.67)
[0.00]
1.60*
(5.70)
[0.00]
Notes: Restrictions are presented in Annexure 6.
* indicates significant at level 5%.
t statistics values are written in () brackets whereas p-values are described in [] brackets
From the above composite SVAR Matrix (Table 5.2), the first row indicates coefficient values for the impact of
the shock on Repo rate significantly impact the NSE, i.e., NSE at a 5% level of significance with a coefficient
value of 0.93. Further, there is a significant impact of policy rate on the growth rate of Gross Domestic Product
(ZRGDP), and on yields of 5-year Corporate Bonds (5YCB) and 5 Year Government Security (5GSEC) with
6 The current policy mandate of RBI is to target CP inflation, due to aggregation issues in CPI series prior to 2012, we consider WPI
as a measure of inflation.
47 the magnitude of 0.71, 0.86 and 0.82. respectively. However, coefficient of WPI is not significant. The impact
of a shock in policy rate is highest for Stock Market, followed by Five Year Corporate Bond, 5 Year
Government Security and Gross Domestic Product.
Similarly, the second row of composite SVAR Matrix shows shock in 91-days Treasury Bill on NSE, growth in
real GDP, 5YCB) and 5GSEC is significant with the magnitude of 1.65, 0.50, 1.33 and 1.32, respectively.
Whereas shock in 91 Days Treasury Bill on WPI is not significant. The impact of a shock in 91 Days Treasury
Bill is highest for Stock Market, followed by corporate bonds, Government securities and GDP.
Finally, the third row of composite SVAR Matrix indicates coefficient values for the impact of the shock on call
money rate significantly impacts NSE. Further, there is a significant impact of policy rate on the growth of real
GDP, 5-year corporate bonds and 5-year Government securities, with the magnitude of 0.87, 1.58 and 1.60
respectively. Whereas the coefficient of WPI is not significant. The impact of a shock in call money rate is
highest for stock market, followed by 5-year Government securities, 5- year Corporate Bond and growth in real
GDP.
It is evident from the above analysis that the three rates i.e. Repo rate, 91 days Treasury bills and call money
rate is providing similar results. Therefore, the Reserve Bank of India can use any of the instruments depending
upon the condition of the economy to make monetary policy more effective and dynamic. Further, this study
reported that policy rates have no impact on wholesale price index. By keeping the mandate of price stability in
the next section a comprehensive empirical analysis has been attempted to know the impact of policy rates on
consumer price index in India with the help of SVAR approach, given that CPI is the focus variable under
inflation targeting.
Figure 5.13: Impulse Response Function of Variables to a Shock in Policy Repo Rate
48 -.12
-.08
-.04
.00
.04
.08
1 2 3 4 5 6 7 8
Response of LNNSE to REPO
-1.5
-1.0
-0.5
0.0
0.5
1.0
1 2 3 4 5 6 7 8
Response of RGDPSGR to REPO
-.8
-.4
.0
.4
.8
1 2 3 4 5 6 7 8
Response of WPI to REPO
-.3
-.2
-.1
.0
.1
.2
1 2 3 4 5 6 7 8
Response of _5YRGSEC to REPO
-.4
-.3
-.2
-.1
.0
.1
1 2 3 4 5 6 7 8
Response of _5YRAAACB to REPO
49 Figure 5.13 highlights the impulse response functions for the reaction of variables under consideration to shocks
in the Repo rate (REPO). Each graph tracks the effect of a one-time shock on the Repo rate and future values of
each sector/instrument specific variable. In the case of NSE, shock in NSE leads to a negative response, thus
indicating that Repo rate shocks harm market stakeholders/ induce a shift in the stock market outcomes.
Initially, for real output, there is a surge in GDP for a period of 1 quarter, and it again sticks to around zero,
indicating no change in output growth. The impact of Repo shock on the inflation index, i.e., WPI, is negative
except for the initial two and half periods. In the GSEC market, 5-year security shows a negative response to the
policy rate shock except for the initial two periods. The five-year high-rated corporate bond indicates an adverse
reaction to the REPO rate shock except for the initial two periods.
The impulse response functions for the reaction of variables under consideration to shocks in 91-days T-bill
Yield (91DAYTBY) is presented in Fig- 5.14. Each graph tracks the effect of a one-time shock on 91-days T-
bill yield and future values of each sector/instrument specific variable. In the case of NSE, shock in NSE leads
to a negative response except for the initial two periods, thus indicating that 91-days T-bill yield shocks market
stakeholders, inducing a shift in the stock market outcomes. The shock of 91-days T-bill yield in GDP is
negative except for the initial five periods. The shock on the inflation index, i.e., WPI, is negative except for the
initial three periods. In the case of G-Secs market, the 5 Year Government security shows an inverse response to
the shock in form of 91-days T-bill yield. The 5-year corporate bond indicates an adverse reaction to the 91-
days T-bill yield shock.
50 Figure 5.14: Impulse Response Function of Variables to a Shock in 91 Days Treasury Bill
-.15
-.10
-.05
.00
.05
1 2 3 4 5 6 7 8 9 10
Response of LNNSE to _91DAYTBY
-2
-1
0
1
2
3
1 2 3 4 5 6 7 8 9 10
Response of RGDPGR to _91DAYTBY
-.8
-.4
.0
.4
.8
1 2 3 4 5 6 7 8 9 10
Response of WPI to _91DAYTBY
-.2
-.1
.0
.1
.2
.3
.4
1 2 3 4 5 6 7 8 9 10
Response of _5YRAAACB to _91DAYTBY
-.2
-.1
.0
.1
.2
.3
.4
1 2 3 4 5 6 7 8 9 10
Response of _5YRGSEC to _91DAYTBY
Response to Cholesky One S.D. (d.f. adjusted) Innovations ± 2 S.E.
51 Figure 5.15: Impulse Response Function of Variables to Shock in Call Money Rate
-.15
-.10
-.05
.00
.05
1 2 3 4 5 6 7 8 9 10
Response of LNNSE to WACR
-2
-1
0
1
2
3
1 2 3 4 5 6 7 8 9 10
Response of RGDPGR to WACR
-.8
-.4
.0
.4
1 2 3 4 5 6 7 8 9 10
Response of WPI to WACR
-.2
-.1
.0
.1
.2
.3
1 2 3 4 5 6 7 8 9 10
Response of _5YRAAACB to WACR
-.2
-.1
.0
.1
.2
.3
1 2 3 4 5 6 7 8 9 10
Response of _5YRGSEC to WACR
52 The impulse response functions for the reaction of variables under consideration to shocks in the call money
rate (WACR) is presented in 5.15. Each graph tracks the effect of a one-time shock on the call money rate and
future values of each sector/instrument specific variable. In the case of NSE, shock in NSE leads to a negative
response except for the first quarter, thus indicating that call money shocks harm market stakeholders/ induce a
shift in the stock market outcomes. The shock of call money rate to growth in real GDP is negative except for
the initial five periods. The shock on the inflation index, i.e., WPI, is negative except for the initial two periods.
In the case of the G-Secs market, the 5-year Government security shows a negative response to the shock in call
money except for the initial five periods. The 5-year corporate bond indicates an adverse reaction to the call
money rate shock except for the initial five quarters.
Interpretation of the Variance Decomposition Results
The forecast error decomposition is the percentage of the variance of the error made in forecasting a variable
due to a specific shock at a given horizon. Thus, the forecast error decomposition is like a partial R
2
for the
forecast error by forecast horizon (Stock and Watson, 2001). The results from variance decomposition analysis
explain the future uncertainty of a time series under consideration due to future shocks into other time series.
Here, it helps understand the impact of the future shock on policy variables under consideration in both the long
run and short-run and distinguish whether it is due to own lag or another variable that feeds into variance.
The forecast error variance has been estimated for eight periods (quarters) to study the decomposition of
variance (Table – 5.3). In this analysis, the first four periods as short run and long run as the 5
th
period onwards.
In the short run, for the NSE, 99.56 percent of Forecast error variance (FEV) is explained by the lag of NSE. So
other variables do not have a significant influence on NSE, i.e., they have a strong exogenous impact. Further,
in a long horizon of 8 periods, 95.15 percent of FEV is explained by NSE. So, a strong exogeneity is exhibited
by other variables in predicting NSE in the future. For GDP, 85.034 percent of FEV is explained by the lag of
output growth itself in the short run. So, other variables do not significantly influence GDP, i.e., they also have
a strong exogenous impact. In a long horizon of 8 periods, 80.35 percent of FEV is explained by output lag. So,
a strong exogeneity is exhibited by other variables in predicting real output growth in the future. In the case of
WPI, the short-run outcomes are explained by the lag of WPI itself, while in the long horizon, 78.90 percent
variance is explained by own lag, while the REPO rate explains 13.0 percent variance followed by 5.7 percent
by 5GSECs. For government securities with a five-year maturity, in the short horizon, own lag of 5GSECs
explains 94.2 percent forecast error variance while in the long horizon, REPO and 5-year CB explain 14.25 and
13.74 percent variance with own lags’ impact reducing to 64.1 percent. Finally, in the case of 5-year CB, in the
53 short horizon, only 9.38 percent of the variance is explained by the own lag while 5-year G-Secs explains 84.7
percent variability in the first period and 72.76 percent till period 4. Further, the exogeneity withers in the long
horizon as other variables continue to explain more than 86 percent of the variance in these high rated corporate
bonds.
Table 5.3: Forecast Error Variance Decomposition
PeriodS.E.REPO NSE ZRGDP WPI 5GSEC 5YCB
Variance Decomposition of
NSE:

1 0.62 0.43 99.56 0.00 0.00 0.00 0.00
2 0.82 0.75 98.50 0.27 0.03 0.25 0.18
3 0.93 1.04 97.74 0.29 0.10 0.51 0.29
4 0.99 1.27 97.11 0.31 0.20 0.78 0.31
5 1.04 1.43 96.54 0.31 0.31 1.07 0.30
6 1.06 1.51 96.02 0.32 0.44 1.38 0.29
7 1.08 1.53 95.56 0.33 0.58 1.70 0.27
8 1.09 1.51 95.15 0.33 0.72 2.00 0.26
Variance Decomposition of
ZRGDP:

1 0.28 0.07 8.60 85.34 0.00 5.86 0.10
2 0.39 0.08 8.31 80.50 0.07 5.73 5.29
3 0.47 0.08 8.35 80.43 0.07 5.75 5.30
4 0.53 0.08 8.37 80.40 0.07 5.75 5.30
5 0.57 0.08 8.39 80.39 0.07 5.75 5.30
6 0.61 0.08 8.40 80.37 0.07 5.75 5.30
7 0.64 0.08 8.41 80.36 0.07 5.75 5.30
8 0.66 0.08 8.42 80.35 0.07 5.76 5.30
Variance Decomposition of
WPI:

1 6.76 5.07 0.00 1.13 86.55 7.14 0.08
2 6.99 2.92 0.00 1.33 87.65 7.76 0.31
3 6.99 2.96 0.00 1.47 87.59 7.59 0.37
4 6.99 4.42 0.00 1.51 86.48 7.17 0.38
5 6.99 6.61 0.02 1.52 84.72 6.70 0.41
6 7.00 9.00 0.07 1.51 82.69 6.27 0.43
7 7.00 11.21 0.15 1.50 80.69 5.94 0.47
8 7.00 13.04 0.28 1.49 78.90 5.74 0.52
Variance Decomposition of
54 5GSEC:
1 1.76 5.32 0.45 0.00 0.00 94.21 0.00
2 2.33 3.02 0.79 0.78 0.00 85.52 9.85
3 2.67 2.91 1.53 1.34 0.01 81.61 12.58
4 2.89 4.66 2.63 1.49 0.01 77.60 13.58
5 3.05 7.55 3.8 1.52 0.01 73.07 13.94
6 3.17 10.92 5.12 1.51 0.02 68.46 13.94
7 3.26 14.25 6.30 1.47 0.08 64.11 13.74
8 3.32 17.24 7.39 1.43 0.21 60.24 13.45
Variance Decomposition of
5YCB:

1 0.44 4.82 1.07 0.00 0.00 84.71 9.38
2 0.59 2.51 1.42 1.25 0.02 79.32 15.45
3 0.70 2.95 2.44 1.60 0.05 76.61 16.32
4 0.78 5.26 3.76 1.65 0.06 72.76 16.48
5 0.85 8.58 5.15 1.64 0.05 68.27 16.29
6 0.91 12.23 6.49 1.59 0.06 63.70 15.90
7 0.95 15.71 7.73 1.53 0.12 59.45 15.43
8 1.00 18.75 8.85 1.48 0.25 55.70 14.94

Cholesky Ordering: REPO, NSE, 5GSEC, 5YCB, ZRGDP and WPI
To conclude this initial part of the analysis, findings show that the variables' response has been significant to the
shock in Repo rate (REPO) except the WPI inflation. In the case of a shock to call money rate, the price
stability variable's response was again insignificant while the other variables showed significant results. The
impact of a shock in the call money rate was highest for the stock market variable (NSE). Finally, for the impact
of a shock in 91 days T-bills rate, the price variable's response remained insignificant. The highest magnitude of
the impact was accounted for the stock market variable, i.e., NSE. All the variables other than price stability
showing a significant response underscores that interest rate transmission has been effective, especially in
accounting for the liquidity alterations within the economy, which may arise market's ability to channelize
surplus funds to the potential investors in an efficient manner. The highest magnitude of response of NSE to
shock in all three policy variables underscores the same. There has been no overheating risk or a serious
implication on the wealth creation ability due to shock in policy variables for the real output. For the private
corporate sector and the government securities, the response to shock in policy interest rates has remained
significant and in line with the theory that interest rate spikes imply bond yields to rise.
55 Dynamics of Private Corporate Investment, Inflation and GDP
7
The purpose of our estimation exercise is to better understand the impact of monetary policy on the real
variables through various transmission channels. It is interesting to note that the Repo rate, call money and 91-
day Treasury Bill rate show a similar trend in transmitting the signal to the economy. It is therefore natural to
further explore the impact of a change in policy rate to inflation, private investments and GDP as the exercise is
important for understanding the dynamics of the relationship between these variables. To explore this issue, the
Repo rate is being used as the policy rate in the SVAR estimation. In that regard, the transmission of the policy
rate to CPI Inflation is explored, as CPI is used as the anchor for inflation targeting, explicitly adopted by the
RBI since 2016 though it was considered an important policy variable from 2014 onwards. Then, the exercise
explores the effect of policy rate on private capital investment. The transmission of monetary policy to the real
economic output happens traditionally through the private capital investment channel and thus, this question is
critical to develop our understanding with regard to the relationship of monetary policy with the real economy.
Finally, the relationship between the Repo rate and growth of real GDP is estimated. The restrictions imposed
on SVAR are presented in Annexure 7.
The SVAR Impulse Responses of all the variables is presented in Fig – 5.16. The SVAR impulse response
functions imply that an increase in the policy Repo rate is associated with a fall in CPI by -0.03, -0.10 & -0.19
in the second, third and fourth quarters, respectively. Further negative impact increases upto seventh quarter and
thereafter negative impact gradually decreases. In response to the first shock, the maximum decline in CPI (-
0.41) occurs with a lag of 5 to 8 quarters. The strong negative effect on CPI is experienced during shock 2
during which the maximum decline of -2.75 occurs in the 5
th
to 8
th
quarters (Table 5.4). Finally, it is assumed
that the impact of repo rate on CPI would decline further after 8
th
quarter if the economy will continue to work
under normal conditions and there would be no bigger policy decision from the government or the RBI in the
long run (Annexure 8 provides estimates until 20 quarters).
8
The accumulated response of PCI reports that during the third shock, an increase in policy Repo rate is
associated with a decrease in PCI by -0.18 , -0.12 and -0.08 in the second, third and fourth quarters,
respectively (Table 5.4, Annexure 8). Thereafter, the response declines gradually to stagnate (Figure 5.16).
During the fourth shock, PCI responds with a decrease of -0.20 in the 2
nd
, 3
rd
, and 4
th
quarters, and thereafter the
7 We would like to thank Dr. Vighneswara Swamy, IBS Hyderabad for his analysis for this sub-section.
8 As the real sector is being considered, estimation has especially been made upto 20 quarters in Annexure 8.
56 response gradually decreases to -0.13 in the 8
th
quarter. The accumulated response of Real GDP Growth Rate
(GDPGR) implies that during the fourth shock, an increase in policy Repo rate is associated with a decrease in
Real GDP Growth Rate (GDPGR) by -0.10 in 2
nd
quarter, -0.83 in the fourth quarter, -1.52 in sixth quarter
and -1.88 in 8
th
quarter. (Table 5.4).
Figure 5.16: SVAR Impulse Responses
57 Table 5.4: SVAR Impulse Responses
Accumulated Response of GDPGR:Accumulated Response of PCI: Accumulated Response of CPI:
PeriodShock1Shock2Shock3Shock4Shock1Shock2Shock3Shock4Shock1Shock2Shock3Shock4
1 1.76 0.00 0.00 0.00 0.32 1.37 0.00 0.00 0.32-0.522.13 0.00
2 2.80-0.01-0.34-0.100.24 1.12-0.18-0.200.53-1.143.72-0.03
3 3.32 0.49-0.42-0.390.17 1.20-0.12-0.200.42-1.644.81-0.10
4 3.52 0.67-0.39-0.830.15 1.14-0.08-0.200.20-1.975.58-0.19
5 3.55 0.66-0.26-1.220.14 1.11-0.05-0.17-0.03-2.266.10-0.26
6 3.54 0.56-0.11-1.520.14 1.10-0.04-0.15-0.21-2.496.44-0.29
7 3.52 0.45 0.03-1.730.14 1.10-0.03-0.14-0.33-2.656.65-0.29
8 3.50 0.37 0.13-1.880.14 1.10-0.03-0.13-0.41-2.756.76-0.27
58 SVAR Responses of GDP growth
Findings
Impact of Repo rate on Inflation
The SVAR impulse response functions suggest that an increase in the Repo rate is associated
with a fall in CPI by -0.03, 0.21, -0.33 & 0.41 for the first shock in the 5
th
, 6
th
, 7
th
and 8
th
quarter,
respectively (Table 5.4). In response to the first shock, the maximum decline of -0.41 occurs
with a lag of 8 quarters (Fig 5.16). The strong negative effect on CPI is experienced during shock
2 during which the maximum decline of -2.75 occurring between 5
th
to 8
th
quarters.
Impact of Repo rate on Private Corporate Investment
The accumulated response of PCI reports that during the third shock, an increase in policy Repo
rate is associated with a decrease in PCI by -0.18 in the 2
nd
quarter (Table 5.4). Thereafter, the
response declines gradually to stagnate at -0.03 from the 7
th
quarter (Fig 5.16). During the fourth
shock, PCI responds with a decrease of -0.20 in the 2
nd
quarter, and thereafter the response
gradually decreases from the 5
th
quarter.
Impact of Repo rate on GDP growth
The accumulated response of growth rate of GDP shows that during the fourth shock, an increase
in policy Repo rate is associated with a decrease in growth rate if GDP by -0.10 in the 2
nd
quarter, and -0.39 in the third quarter. During the 4
th
quarter, the response of growth rate of GDP
is -0.83, and -1.88 during the 8
th
quarter (Table 5.4, Annexure 8).
To conclude, given the importance of monetary policy in reviving economic growth during times
of distress, this study offers an empirical assessment of the relationship between repo rate,
inflation, private corporate investment and growth of real GDP. Following the SVAR model, this
study finds evidence that increase in Repo rate has a negative effect on CPI inflation, with a lag
of two-quarters and a moderating impact on inflation with a lag of five-quarters. The SVAR
impulse response functions suggest that an increase in the Repo rate is associated with a fall in
CPI by -0.03 for the first shock in the 5th quarter. The study reveals that private corporate
investment responds to a positive shock to Repo rate with a decline during the 2
nd
quarter. The
accumulated response shows that during the third shock, an increase in Repo rate is associated
59 with a decrease in private corporate investment by -0.18 in the 2
nd
quarter. During the fourth
shock, private corporate investment responds with a decline of -0.20 in the 2
nd
quarter. The
response of private corporate investment steadily decreases after the 5
th
quarter.
In a special estimation, results reveal that GDP growth responds to 1-percentage point Repo rate
rise (impulse) with a decline of about -0.31 percent in the fourth quarter, and -0.12 percent in the
8
th
quarter. Every 100-basis points reduction in Repo rate can lead to a rise in private corporate
investment by around 18 basis points in the 2
nd
quarter.
Evaluating the Transmission Mechanism of Monetary Policy in India
9

The previous two sub-sections looked at the impact of changes in repo-rates on various
macroeconomic variables. While the first sub-section concluded that the monetary mechanism
was not contemporaneous and that Repo rate, 91-day Treasury bills and call money rates
exhibited similar tends , the second sub-section provided the relationship between these variables
with a lag thereby explaining the dynamics of adjustment in real economy as a response to
changes in Repo rates. The conclusion of both sections was that there does exist a
macroeconomic relationship between monetary policy and the real economy. In this sub-section,
a baseline model is constructed which is then augmented by considering various variables to
capture the impact through different monetary channels. The estimation is carried out through
SVAR, closely following Khundrakpam and Jain (2012) and Mohanty (2012).
The previous results have used the Repo rate, discount rate on 91-day Treasury bills and the
weighted average call money rate. The baseline model is constructed by including growth of real
GDP growth ((ZRGDP), CPI inflation (CPI)) and repo rate (REPO). Then the baseline model is
extended by including variables, each capturing one channel of monetary transmission, in order
to assess the effectiveness of that channel. Finally, we use the model to estimate the response of
GDP growth rate to a 200-bps shock to repo rate, in order to estimate the models’ implication for
the response of GDP growth rate to a big monetary policy push.
9 We would like to thank Dr. Lokendra Kumawat, Ramjas College, Delhi University for his analysis for this sub-
section.
60 In this estimation, we further extend the model by including 91-day Treasury Bill rate as the RBI
announces various measures in addition to the Repo rate, even though it has been the main
instrument during the last two decades. Therefore, one would expect that short-term interest rates
such as call money rate and Treasury bills rate tend to capture the effects of monetary policy in
an all-encompassing pattern, absorbing the effects of other measures in a better way. Of these
two short term money market rates, call rate is more volatile than 91-day Treasury bill rate, even
though the two move together in the long run (Kumawat and Bhanumurthy, 2018). Therefore, in
this exercise, 91-day Treasury bills (T91) rate has also been explored in the baseline and
augmented model in addition to Repo rate, as the policy variable.
i. SVAR Baseline Model
In the baseline model, the variables are (in the order in which these are taken in estimations): real
GDP growth (ZRGDP), CPI inflation (CPI) and Repo rate (REPO)/91-day T-bill yield (T91).
This is the simplest specification to study the effect of monetary policy on the real variables, as it
includes the real GDP growth, inflation rate and the policy rate. As measure of inflation, we take
CPI inflation since that is the measure targeted by the RBI. The identification restrictions
imposed in the SVAR are as follows: (i)no contemporaneous effect of shocks in inflation and
interest rates to GDP growth (ii) only GDP growth has a contemporaneous impact on inflation,
and (iii) no contemporaneous impact of GDP growth and inflation shocks to policy interest rate,
implying monetary policy reacts to GDP and prices only with some lags. These are in line with
those in the existing literature (e.g., Kundrakpam and Jain, 2012).
Two variants of the baseline model are considered, as discussed: the first variant considers the
Repo rate while the second version considers the 91-Day Treasury yields as an alternative to
Repo Rate.
Table 5.5: Forecast error variance decomposition at 12 lags - Baseline model with Repo rate
Variable SE Contribution to Forecast error variance
Shock1 Shock2 Shock3
GDP 2.15 91.56 0.11 8.33
CPI 3.41 1.13 98.82 0.05
REPO 1.10 0.09 1.24 98.66
Table 5.6: Forecast error variance decomposition at 12 lags - Baseline model with TBR91
Variable SE Contribution to Forecast error variance
Shock1 Shock2 Shock3
GDP 2.17 80.67 1.75 17.56
61 CPI 3.39 2.31 97.10 0.58
T91 1.43 19.97 6.48 73.54
These results from forecast error variance decomposition presented in Tables 5.5 and 5.6 indicate
that the policy rate accounts for some fraction of forecast error variance of GDP growth in 12
th
quarter. This result is much stronger with T-bill rate than with Repo rate.
Figure 5.17 gives the response of GDP growth to one standard deviation shock to Repo Rate and
the Treasury Bill rate, from the baseline model. It shows that a positive shock to repo rate leads
to a decline in GDP growth, and the effect peaks in about 4 quarters. A similar impact is
observed for a shock to T-bill rate, and the magnitude of the impact is higher when compared to
a shock to Repo rate.
Figure 5.17: Response of GDP Growth to one s.d. shock to Repo & TBR 91 rate
O bject 79O bject 82
ii.Augmented Models: Evaluating the channels of monetary transmission
On the basis of the two baseline models, several iterations are attempted by adding other key
macroeconomic variables aimed at identifying channels of monetary transmission and checking
the robustness of these results. These iterations are carried out by adding one-by-one different
variables representing different channels of monetary transmission. The two models are
estimated for each variable of this type: one taking the variable as exogenous and another one
taking it endogenous. Taking a channel-specific variable as exogenous blocks the dynamic
interactions of that variable with the other variables, thus blocking that channel; while taking it
as endogenous allows that channel to operate. The differences between these two models thus
62 provide information about the effectiveness of that channel. Identification of the structural
shocks is based on the same set of restrictions as in the baseline models, with no restriction on
the contemporaneous effect of the other variables on the channel variable.
Among the channel-specific variables, the non-food credit growth (GNFC) is taken to capture the
impact of any change in monetary policy through the credit channel. Similarly, the use of the
variable (quarterly growth of) BSE Sensex (dlog(BSE)) is taken to capture the impact through
the asset price channel. Typically, both these channels have been important channels of monetary
transmission for some economies. The exchange rate channel of monetary transmission is
estimated using the variable NEER, again in quarterly growth form (dlog(NEER).
Credit channel
The credit channel is studied through the non-food credit. In order to see the role of non-food
credit in the transmission of this shock, the baseline SVARs with Non-food credit growth
(GNFC) is estimated (Fig – 5.18). As discussed above, when GNFC is taken as exogenous the
transmission through this channel is blocked, and therefore the difference between the response
functions of GDP growth to one standard deviation shock to the policy rate as estimated from
these two models highlights the role of GNFC in this transmission. The positive shock to the
policy rate leads to a decline in GDP growth. The peak effect is observed in the fourth quarter
and this effect is stronger with Treasury bill rate than with Repo rate.
Figure 5.18: Response of GDP through the Credit Channel
O bject 85O bject 88
63 Comparing the responses of the GDP growth to policy rate shocks from the GNFC-exogenous
and GNFC-endogenous models, we find that for the first three/four quarters the two are almost
identical, but thereafter the response is higher in the GNFC-exogenous model. These results
imply that there are issues in the transmission of Treasury bill shocks through NFC growth, i.e.,
the credit channel. This is important as credit growth typically is considered to be one of the
traditional channels of monetary transmission. Figure 5.19 presents the data on CPI Inflation, 91-
day T-bills, growth in GDP and growth in Non-Food Credit. The data shows a systematic
reduction in credit growth irrespective of the state of the economy.
Figure 5.19: Baseline Variables and Growth of Non-Food Credit
O bject 90
Asset price channel
The effect of this channel is studied through dlog (BSE) i.e., quarterly growth rate of BSE
Sensex, which is a measure of stock returns. Figure 5.20 shows that a positive shock to Treasury
-bill rate leads to a decline in stock returns from second quarter onwards and the effect peaks in
the fourth quarter. Again the magnitude of the impact is greater for 91-day Treasury-bill rate.
This is consistent as any shock in bond market will have an impact on the equity markets which
will subsequently have an impact on the overall growth rate.
64 Figure 5.20 Response of GDP through the Asset Price Channel
O bject 93O bject 96
Comparing the response of GDP growth to Treasury-bill rate shock from the dlog(BSE)-
exogenous and dlog (BSE) - endogenous specifications, we find that up to 10
th
quarter, the
response is higher in the dlog(BSE)-endogenous specification, indicating an important role of the
asset price channel.
Exchange rate channel
The effect of this channel is studied through dlog (NEER), i.e., quarterly rate of nominal
appreciation of Indian rupee (Fig 5.21). Interestingly, a positive shock to the policy rate leads to
a depreciation of the rupee immediately, though it rebounds sharply in the next quarter. Further,
the response of GDP growth to policy rate shock continues to be higher in the dlog (NEER)-
endogenous specification than that in the corresponding dlog (NEER)-exogenous specifications
even after 12 quarters, highlighting the role of exchange rate channel in monetary transmission.
The results are similar for the two estimations. As in the other cases, the quantum of impact is
lower for a Repo shock than a 91 Day Treasury-bill shock.
65 Figure 5.21: Response of GDP through the Exchange Rate Channel
O bject 98O bject 100
Figure 5.22 illustrates the limited change in the nominal effective exchange rates even as other
variables have been volatile. The results obtained above are contrary to conventional wisdom,
however, and can probably be explained, given that the exchange rate policy of the RBI has been
classified by some scholars as managed float. That is, there is a range in which RBI attempts to
keep the rupee against the dollar, given the political circumstances, though the intervention is
generally resorted when volatility is high. Therefore, any intervention by the RBI distorts the
movements of the nominal exchange rates and this distortion could be the reason for the contrary
results obtained above.
Figure 5.22: Change in NEER, CPI Inflation, T-bill Yields and GDP Growth Rates
O bject 102
66 Interest rate channel
On the basis of the above exercise, a composite model is estimated to capture the combined
effect of the three channels. Again, SVAR models were estimated with all the three models
exogenous and all of them endogenous.. The basic idea here is that the difference between the
impulse response of GDP growth from all-three-exogenous and all-three-endogenous models
will give the role of the three channels taken together. The remaining response then would be
attributable to the channels other than these three, and it seems that in India interest rate channel
is the only other important channel.We find that up to 8
th
quarter the response is higher in all-
endogenous specifications than in all-exogenous specifications.
10
In fact, at the peak, i.e. in the
fourth quarter, the response in the all-endogenous specification is about 50% higher than the all-
exogenous specification when Tbill rate is taken as the policy rate. When the repo rate is used as
the policy rate this figure is about 25%. In line with the pattern observed in the baseline and
individual-channel-estimations, the responses of GDP are similar in the repo rate and the
Treasury bill-rate specifications, even though the quantum of impact of a repo shock is lower
than the 91-day Treasury bill rate shock
Figure 5.23: Response of GDP to Shocks: Composite Model
O bject 104O bject 107
The results show that up to 8
th
quarter the response is higher in all-endogenous specifications
than in all-exogenous specifications.
11
In fact, at the peak, i.e. in the fourth quarter, the response
10 For identifiton of strufturiln shofks, we use the sime set of restriftons is in the fhinneln-viriiblne-
endogenous spefiifitons ibove, with one idditoniln set of restriftons: the three fhinneln-spefiif viriiblnes do
not hive iny fontemporineous efeft on eifh other.
11 For identification of structural shocks, we use the same set of restrictions as in the channel-variable-endogenous
specifications above, with one additional set of restrictions: the three channel-specific variables do not have any
contemporaneous effect on each other.
67 in the all-endogenous specification is about 50 percent higher than the all-exogenous
specification when Treasury bill rate is taken as the policy rate. When the Repo rate is used as
the policy rate this figure is about 25 percent. In line with the pattern observed in the baseline
and individual-channel-estimations, the responses of GDP are similar in the Repo rate and the
Tbill-rate specifications, even though the quantum of impact of a Repo shock is lower than 91-
day Treasury bill shock.
Figure 5.24: Response of INFCPI to Shocks: Composite Model
O bject 110O bject 112
Figure 5.24 presents the response of INFCPI to a Repo and a TBR91 shock. The key observation
is that the impact across a shock to either variable is identical and it peaks in period 3. However,
a Repo rate shock has a significantly lower impact on INFCPI than compared to a Treasury bill
shock. This feature is consistent with what was observed for the response of growth in real GDP.
To conclude, in order to see the implications of the models for the possible response of GDP
growth to a large monetary stimulus in terms of Repo rate reduction, an estimation is made for
the accumulated response of different variables to a 200 points negative shock to Repo rate in
Table 5.7. This is done by estimating the all-endogenous model discussed above, with Repo rate
as the interest rate variable. The projections were obtained by scaling the impulse response
function obtained from the SVAR model in such a way that the shock to the repo rate is (-) 200
bps.
12
However, it would be best to interpret the result on a four quarter projections because in
12 It must be noted that while studying effects of such large shocks it may not be appropriate to draw inferences too
much ahead in future though estimation has been made upto 12 quarters. Therefore, in the text, discussion has been
restricted to 4 quarters
68 the long run, the variables may be impacted by various developments, global and domestic,
including initiatives by the Government and the RBI.
Table 5.7: Accumulated response of different variables to 200 bps negative shock to Repo rate
(GNFC, Dlog (BSE), Dlog (NEER) endogenous)
Perio
d GDP CPIREPONFCDLOG(BSE)DLOG(NEER)
4 2.21 0.24-6.122.66 0.04 0.03
8 4.64 0.32-8.829.94 0.10 0.05
12 5.46 0.60-9.8918.53 0.12 0.06
Table 5.7 shows the accumulated response of real GDP growth to a 200-bps negative shock to
repo rate. It shows a 2.21 percent increase after 4 quarters. The overall impact of the change
continues beyond 12 quarters. The impact of a shock on inflation over a longer period seems to
be muted which suggests a limited role of monetary policy in affecting future inflation. This
probably could be due to the greater role of food prices in shaping up price expectations than
monetary anchoring of inflation expectations.
To conclude this sub-section, dynamic interrelations among GDP growth, CPI inflation and
policy rate, using structural VAR models were examined. The results were obtained using Repo
rate as the policy rate as also using 91-day Treasury bill rate, as an alternative. There is a
substantial impact of policy rate shocks to the GDP growth, with the peak effect coming in the
fourth quarter. The composite models attempted to identify the robustness of the fit to facilitate
projections. Finally, the response of real GDP growth to a 200 bps negative shock to Repo rate
was estimated and found that the cumulative effect after four quarters will be about 2.2
percentage points.
69 Section 6
Conclusion
The purpose of the study was to analyse how macroeconomic variables respond to monetary
policy and to better understand the transmission mechanism that governs it. The monetary policy
has evolved over the years along with the objectives of monetary policy. Initially, the objective
of monetary policy was to ensure price stability but since 2008, financial stability is part of the
monetary policy objectives.
In India, the RBI has been making efforts to develop the financial markets since 1992 and ensure
better integration of the markets. The RBI adopted a multiple indicator approach in 1998, after
the Asian crisis, in which inflation was one of the indicators, along with other variables from the
fiscal, financial and external sector. In 2016, India formally adopted inflation targeting as a
monetary policy objective. Thus, during this period, there was transition from multiple
indicators, including wholesale prices, to focus on consumer prices.

The channels of monetary policy transmission are interest rates, bank credit, asset prices and
exchange rates. There has been extensive empirical literature on estimating the transmission
mechanism of the monetary policy through these channels. However, the RBI has repeatedly
observed that the policy impulses have not been transmitted to the market, especially through the
banking system, and has been initiating policy measures to ensure efficient and quick
transmission, especially with respect to movement in the lending rate of banks.
In this study, Structural VAR has been used to study the relationship between various macro-
variables and the policy rate. This study has explored the transmission mechanism by
considering shocks in repo rate, call money rate and 91 Treasury bill rate. The impulse response
functions and variance decomposition analysis were undertaken to study the monetary
transmission in India using various model specifications. We begin by considering the impact of
a shock whether in call money rates, Repo rate and 91-day Treasury bills on different macro-
variables. This is followed by an exercise that focuses exclusively on the impact of Repo rate on
rate of growth of GDP, private corporate investment and inflation. Finally, a baseline model of
70 SVAR is estimated and then augmented by adding different transmission channels to understand
the impact of monetary policy in India.
In an interesting finding, impact of Repo rate, weighted call money rate and 91 day Treasury
bills yield similar results. The augmented models with Repo rate and 91-day Treasury bills are
estimated subsequently, and we observe that while the direction of the impact is the same for the
Repo rate versions of the models, however, the magnitude of the impact is lower than the
specifications which include 91-day Treasury Bill rates. Therefore, the baseline and augmented
modelling exercise illustrates that monetary transmission is partial – and that changes in
Treasury bill yields have far more impact on macroeconomic aggregates than changes in the
Repo rate. This makes sense given that government securities serve as a benchmark for corporate
debt and cost of capital in the country. The low transmission of changes in Repo rate as
compared with 91-day Treasury bills is due to multiple factors including the policy of small
savings rate that impacts the long end of the yield curve.
The macro variables used in the analysis to estimate the impact of the Repo rate were chosen
after considering the correlation matrix, pair-wise granger causality, trend analysis and intuition
based on economic logic and monetary theory. Finally, SVAR estimation was based on growth
rate of GDP (real sector), Prices (WPI and CPI), asset prices (BSE and NSE), interest rates (91
days Treasury bIlls, 5 year government securities and 5-year triple A rated corporate bonds),
credit (non-food credit) and exchange rates (NEER). The results reveal that the Repo rate does
impacts the macro variables, especially, growth, private corporate investment and prices.
In a hypothetical case of a 200-bps negative shock to Repo rate, the real GDP growth would be
enhanced by 2.21 percent after 4 quarters. In another estimation, following a different
specification of SVAR, the impact for a 100 basis negative shock in the Repo rate, growth rate of
GDP, would record a rise of 0.31 percent in the fourth quarter. The impact of a shock on
inflation is muted which implies a limited role of monetary policy in affecting future inflation.
One possible reason for this could be the high weights of food and fuel in India’s CPI measure
which is less influenced by changes in interest rates. Thus, the policy Repo rate does have an
impact on the real sector of the economy, implying that transmission is taking place, though
muted, in the economy
71 For the last three decades, RBI has tried to improve the issue of monetary transmission, however,
it has not had the extent of the impact that was desired. The transmission of changes in Repo
rates to lending rates is often too slow which blunts the ability of monetary policy to stimulate
the economy during economic slowdowns. Though RBI has made recent amends by getting
banks to offer more products that are linked with the Repo rates, the lack of transmission is an
outcome of the high small savings rates that are offered to depositors which restricts the ability
of banks to reduce their deposit rates. The other reason could be that about two-third of total
outstanding formal credit is extended by instruments that are impacted by the Repo rates while
one-third of the credit is extended through NBFCs, Micro-Finance Institutions and other
financial intermediaries which are not linked to the Repo rates.
Recommendations
In view of the study conducted, the following recommendations are being made -
Given that the transmission mechanism is muted/partial, monetary policy has a lower
impact than it would due to small savings rates which act as a de-facto floor on deposit
rates. Therefore, linking deposit rates on small savings rates will be effective to assist
with monetary transmission.
The impact of short-term yields (91-day Treasury bills) is significantly higher than the
Repo rate. Therefore, the RBI could consider moving to a similar framework as in the US
Fed where it sets a target range for the US Federal Securities as an instrument to set
interest rates in the economy.
The limited impact of policy rate changes on CPI further points to the need to relook at
the target for inflation. The probable reason could be that the present CPI uses 2011-12
weights from the then CES Survey, but consumption basket is likely to have shifted
significantly over the years. The composition of the basket, given the weightage of food
and fuel, needs to be examined. There should be further research on the appropriate
indicator for inflation going forward.
The monetary policy framework also requires tweaking given its sole focus on inflation
targeting. What is needed is a dual mandate with explicitly defining the range of India’s
potential growth rate to ensure RBI and the MPC can maintain an accommodative stance
as and when needed, giving weightage to growth, especially in a young demographic
country like India.
72 Many NBFCs have emerged as important institutions that contribute significantly to
credit creation, it is important to link their rates with the Repo rates to ensure monetary
transmission.
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84 Annexure 1: Brief Review of Literature on Monetary Transmission – Select Studies YearAuthorsPaper
Title
Period of
Study
Statistical
Techniques
Observations and
Variables taken
into consideration
Results and Conclusions
2010Rudrani,
Bhattachar
y, Ila
Patnaik
and Ajay
Shah
Monetary
policy
transmissio
n in an
emerging
market
setting
(IMF
Working
Paper)
Source:
https://ww
w.imf.org/
~/media/W
ebsites/IM
F/imported
-full-text-
pdf/externa
l/pubs/ft/w
p/2011/_w
p1105.ashx
1997-2009 Vector Error
Correction
Model
Price series (WPI),
Exchange rate,
Interest rate (91-day
treasury bills rate),
IIP as proxy for
output,
US PPI (producer
price index) as a
measure of world
tradeables inflation,
3-month treasury
bills rate of US for
capturing the
monetary policy
stance of rest of the
world
This paper finds that the monetary policy
transmission in India is weak. In India
evidence of incomplete but statistically
significant exchange rate pass-through is
found. However, given a strong, though
incomplete exchange rate pass-through,
interest rates can impact inflation
through the exchange rate.
2017Ashima
Goyal and
Deepak
Kumar
Agarwal
Monetary
transmissio
n in India:
Working of
price and
quantum
channels
(Indra
Gandhi
Institute of
Developme
nt
Research)
Source:
http://www
.igidr.ac.in/
pdf/publica
tion/WP-
2017-
017.pdf
2002-2017 OLS
regressions
of event
windows
around
change in
repo rates
Repo Rate,
Call money market
rate,
Collateralized
borrowing and
lending obligations,
T-bills and G-Secs,
Liquidity
Adjustment Facility
(LAF) injection and
absorption,
Cash reserve ratio,
Open market
operations,
Foreign exchange
market intervention,
Market stabilisation
scheme
The results find the interest rate channel,
with repo rate as the policy rate, as the
most effective medium to influence
market rates. The speed of response is
faster where there is more market depth.
Also, size of the pass-through rises when
rate and quantity variables are in sync.
2011Jeevan Credit 2001:Q3- OLS Nominal bank credit,The paper examined the operation of Annexure 2: Complete Data Description
S. No.Name of the variableUnit of Measurement Data Source
1 Gross Domestic Product (GDP)
Spliced adjusted level at Constant 2011-12
Prices (in Crore)
National
Accounts
Statistics
(NAS)
2 IIPSpliced Growth Rate (Base: 2011-12 = 100)CSO
4 Money Supply Narrow and Broad - Both at Level RBI
5 Gross Capital Formation Level and % of GDP at Current PricesNAS
6 Export Level and % of GDP at Current PricesNAS
7 ImportLevel and % of GDP at Current PricesNAS
8 Repo Rate Average of Quarter Starting from Apr-JuneRBI
9
Real Effective Exchange Rate
(REER)
Spliced Index Number, (Base: 2004-05 =
100) at Trade Based Weight
RBI
10
Nominal Effective Exchange
Rate (NEER)
Spliced Index Number, (Base: 2004-05 =
100) at Trade Based Weight
RBI
11Exchange Rate (INR/USD)In INR/USDRBI
12Foreign Direct Investment (FDI) Gross and Net FDI in Crore
EPW
Research
Foundation
13
Foreign Institutional Investment
(FII)
Net FII in Crore
EPW
Research
Foundation
14Bombay Stock Exchange (BSE)
Quarterly Average Index at Base: 1983-
84=100
RBI
15National Stock Exchange (NSE)Quarterly Average Index at Base: 1995=1000RBI
16Non-Food Credit (NFC)Quarterly Average in Crore RBI 17Total DepositQuarterly Average in Rs. Crore RBI
18Total CreditQuarterly Average in Rs. Crore RBI
19Prime Lending Rate Level in percent
RBI and
Commercial
Bank
20Private Corporate Investment
% of GDP at Current Price and Share in
Total GCF derived from Annual Current
Price NAS data
NAS
21Household Investment
% of GDP at Current Price and Share in
Total GCF derived from Annual Current
Price NAS data
NAS
22Public Investment
% of GDP at Current Price and Share in
Total GCF derived from Annual Current
Price NAS data
NAS
23CPI
Spliced Growth Rate Based on (Base: 2011-
12=100).
RBI
From 2010 January CPI-combined and prior
to that CPI-IW
24WPI
Spliced Growth Rate Based on (Base: 2011-
12 = 100).
RBI
25G-Sec/Treasury Bill Yields
Quarterly Average- 91 Day, 364 Day, 5 Year
G-Sec, 10Year G-Sec
EPW
Research
Foundation
26
Weighted Average Call Money
Rate
Quarterly Average RBI
27Commercial Paper
Quarterly Average High and Low Rate of
Interest
EPW
Research
Foundation
28Certificates of Deposit
Quarterly Average High and Low Rate of
Interest
EPW
Research
Foundation
295 Year AAA Rating Corporate
Bond
Quarterly Average Yield Fixed Income
Money
market and Derivatives
Association
of India
30CRRQuarterly Average in percent RBI
31Reverse Repo rateQuarterly Average in percent RBI
32Bank rateQuarterly Average in percent RBI
Note- Quarter is starting from Apr- Jun
All the growth rate is taken from corresponding previous quarter Annexure 3a: CROSS CORRELATION MATRIX
CPIWPINEERNFC NFDINFIINSE PLRREERREPORREPO PCIHINVT364
CPI 1
WPI 0.4 1
NEER 0 0.4 1
NFC 0 -0.3-0.8 1
NFDI -0.1-0.4-0.70.8 1
NFII 0.2 0.1-0.10.2 0 1
NSE 0 -0.3-0.7 1 0.8 0.2 1
PLR 0.2 0.2 0.6-0.8 -0.6-0.2-0.7 1
REER -0.1-0.2-0.40.8 0.7 0.2 0.9-0.7 1
REPO 0.1 0.1-0.2 0 0 0 0 0 0 1
RREPO 0 -0.1-0.50.5 0.3 0.1 0.4-0.50.3 0.8 1
PCI 0 -0.10.3 0.1 0.1 0.1 0.3-0.10.2-0.2-0.2 1
HINV 0.1 0.1-0.60.4 0.3 0.2 0.2-0.30.3 0.4 0.5 -0.7 1
T364 0.1 0.1-0.2 0 0 -0.1 0 0.2-0.10.4 0.3 -0.30.1 1
T91 0.1 0.1-0.30.1 0.1 0 0 0.1-0.10.4 0.4 -0.30.2 1
WACR 0 0.1-0.2 0 0 -0.1 0 0.1-0.10.4 0.4 -0.30.2 0.9
ZRGDP 0 -0.3-0.7 1 0.8 0.2 1 -0.80.9 0 0.4 0.2 0.3 0
DLOGBSE -0.3 0 0.2-0.1 -0.10.1 0 -0.10.1-0.1-0.1 0.4-0.40.1
DLOGNEER -0.2-0.10.2-0.1 -0.10.2-0.1 0 0.1-0.2-0.3 0.1-0.2-0.2
3a: Correlation Matrix Contd.
T91WACR
ZRGD
P DLOGBSE DLOGNEER
T911.0
WACR 1.0 1.0
ZRGDP 0.0 0.0 1.0
DLOGBSE 0.0 0.0 0.0 1.0
DLOGNEER -0.2-0.2 -0.1 0.3 1.0 Annexure 3b: Cross Correlation Matrix 1998Q1-2002Q4
RRGDP
WP
I
CPIPCI
NEE
R
BSE
NS
E
PL
R
DR
CM
R
5YGSE
C
5YC
B
91T
B
NFC
RR 1.0
GDP -0.21.0
WPI -0.2-0.51.0
CPI 0.1-0.70.31.0
PCI -0.30.2-0.30.21.0
NEER -0.40.50.0-0.5-0.41.0
BSE -0.60.50.1-0.50.20.51.0
NSE -0.60.60.0-0.60.10.61.01.0
PLR -0.1-0.40.00.40.4-0.5-0.2
-
0.3
1.0
DR -0.3-0.10.20.30.30.20.20.30.01.0
CMR -0.50.20.4-0.10.10.40.60.6-0.20.51.0
5YGSE
C
-0.5-0.30.50.50.30.10.20.20.20.80.81.0
5YCB -0.5-0.30.50.40.30.10.30.30.10.80.81.01.0
91TB -0.6-0.10.50.20.20.30.50.5-0.10.70.90.90.91.0
NFC -0.10.7-0.2-0.8-0.10.20.40.4-0.1-0.6-0.2-0.6-0.6-0.41.0 Annexure 3c: Cross Correlation Matrix 2003Q1-2007Q4
RRGDP
WP
I
CPIPCI
NEE
R
BS
E
NS
E
PL
R
DR
CM
R
5YGSE
C
5YC
B
91T
B
NFC
RR 1.0
GDP 0.01.0
WPI -0.10.41.0
CPI 0.40.10.01.0
PCI 0.30.4-0.20.41.0
NEER 0.10.5-0.30.20.91.0
BSE 0.70.3-0.20.50.80.61.0
NSE 0.70.3-0.20.50.80.61.01.0
PLR 0.90.3-0.20.30.60.40.80.81.0
DR 0.70.2-0.10.30.50.40.80.80.81.0
CMR 0.50.20.10.50.50.30.60.60.50.41.0
5YGSE
C
0.50.3-0.10.60.90.70.80.80.70.50.71.0
5YCB 0.50.3-0.10.60.90.70.80.80.70.50.71.01.0
91TB 0.70.30.00.50.80.60.90.90.70.60.90.90.91.0
NFC -0.30.20.00.30.60.60.20.2
-
0.2
-0.20.30.60.60.41.0 Annexure 3d: Cross Correlation Matrix 2003Q1-2007Q4
RRGDP
WP
I
CPIPCI
NEE
R
BS
E
NS
E
PL
R
DR
CM
R
5YGSE
C
5YC
B
91T
B
NFC
RR 1.0
GDP 0.01.0
WPI -0.10.41.0
CPI 0.40.10.01.0
PCI 0.30.4-0.20.41.0
NEER 0.10.5-0.30.20.91.0
BSE 0.70.3-0.20.50.80.61.0
NSE 0.70.3-0.20.50.80.61.01.0
PLR 0.90.3-0.20.30.60.40.80.81.0
DR 0.70.2-0.10.30.50.40.80.80.81.0
CMR 0.50.20.10.50.50.30.60.60.50.41.0
5YGSE
C
0.50.3-0.10.60.90.70.80.80.70.50.71.0
5YCB 0.50.3-0.10.60.90.70.80.80.70.50.71.01.0
91TB 0.70.30.00.50.80.60.90.90.70.60.90.90.91.0
NFC -0.30.20.00.30.60.60.20.2
-
0.2
-0.20.30.60.60.41.0 Annexure 3e: Cross Correlation Matrix 2013Q1-2018Q4
RRGDP
WP
I
CPIPCI
NEE
R
BSENSEPLRDR
CM
R
5YGSE
C
5YC
B
91T
B
NFC
RR 1.0
GDP 0.31.0
WPI 0.10.61.0
CPI 0.60.60.31.0
PCI 0.40.1-0.30.41.0
NEER -0.5-0.4-0.3-0.50.31.0
BSE -0.7-0.30.0-0.8-0.70.31.0
NSE -0.7-0.40.0-0.8-0.70.31.01.0
PLR 0.90.1-0.30.50.5-0.4-0.7-0.71.0
DR 0.90.2-0.10.70.5-0.5-0.8-0.80.91.0
CMR 0.90.30.10.80.4-0.6-0.8-0.80.91.01.0
5YGSEC0.90.40.10.70.1-0.7-0.6-0.50.70.80.81.0
5YCB 0.90.40.10.70.1-0.7-0.6-0.50.70.80.91.01.0
91TB 0.90.30.10.80.4-0.6-0.8-0.80.91.01.00.90.91.0
NFC 0.50.30.40.6-0.3-0.8-0.3-0.30.30.50.60.70.70.61.0 Annexure 4
Unit Root Test Results for all the Variables
Markets and
Instruments
Serial
Codes Variables Stage ADF - AICADF -SCPP
Chosen order of
Integration (I)
Policy Interest Rate Repo Rate (RR) Level with
Intercept & Trend
0 1 0 0
A. Stock Market A.1. NSELevel with
Intercept & Trend
1 1 1 1
A.2. BSELevel with
Intercept & Trend
1 1 1 1
B. Deposit and Lending
Rates
B.1. 91 Days- 6 months
Deposit Rate
(DR91)
Level with
Intercept & Trend
0 0 0 0
B.2. 1-2 years Deposit
Rates (DR2Y)
Level with
Intercept & Trend
1 1 1 1
B.3. Lending Rates
-Prime Lending
Rate (PLR)
Level with
Intercept & Trend
1 1 1 1
C. G-Sec. Market
Instruments
C.1. 91 days - G-Sec
Rates (T91)
Level with
Intercept & Trend
1 1 1 1
C.2. 364 days Gsec Level with 1 1 1 1 Rates (T364) Intercept & Trend
C.3. 5 Year GSec Rates
(5YGSEC)
Level with
Intercept & Trend
1 1 1 1
C.4. 10 Year GSec
(10YGSEC)
Level with
Intercept & Trend
1 1 1 1
DD.1 Call money
(WACR)
Level with
Intercept & Trend
1 1 1 1
D.2 Lower CP rate
(LCP)
Level with
Intercept & Trend
1 1 1 1
D.3 Lower CD rate
(LCD)
Level with
Intercept & Trend
1 1 1 1
E. Bond Market E.1. Bond Market AAA
rated (5YCB)
Level with
Intercept & Trend
1 1 1 1
F. Prices F.1. Consumer Price
Index (CPI)
Level with
Intercept & Trend
1 1 1 1
F.2. Wholesale Price
index (WPI)
Level with
Intercept & Trend
0 0 1 0
G. – External SectorG.1. Exchange Rate - ln
transformed (ER)
Level with
Intercept & Trend
1 1 1 1
G.2. (ln
transfor
med)
Nominal Effective
Exchange Rate –
log transformed
(lnNEER), (NEER)
Level with
Intercept & Trend
1 1 1 1
G.2. (in
absolut
e
figures)
Real GDP (in
crores)
Level with
Intercept & Trend
1 1 1 1
H. Real Sector H..1.Real GDP -growth
rate (ZRGDP)
Level with
Intercept & Trend
1 1 0 1
H.2. IIP-growth rate
(ZIIP)
Level with
Intercept & Trend
1 1 0 1
I. Non-Food Credit and
Deits
I.1. Non-Food Credit
-growth and
Level with
Intercept & Trend
1 1 1 1 absolute: (LNNFC)
&
I.2. Non-Food Credit –
crores (NFC)
Level with
Intercept & Trend
1 1 1 1
I.3. Total Deposits-
growth (ZTD)
Level with
Intercept & Trend
2 1 1 1
I.4. Total
Deposits(crores)
Level with
Intercept & Trend
1 1 1 1
Note: * indicates lag order selected by the criterion assuming a 5% level of significance. AIC: Akaike information criterion, SC:
Schwarz information criterion, and PP: Phillips-Perron test statistic Annexure 5: Pairwise Granger Causality test on all variables
Null Hypothesis: Repo Causes Variable

Lags F-Statistic ProbDecision
REPO NSE1 3.23047 0.0765 Causality Exists***
REPO DR911 6.23437 0.0148 Causality Exists **
REPO PLR6 3.2808 0.0077 Causality Exists*
REPO 5GSEC1 5.62649 0.0204 Causality Exists **
REPO 10GSEC1 9.72209 0.0026 Causality Exists*
REPO LCP2 4.71152 0.0121 Causality Exists**
REPO LDP 4 3.62199 0.0102 Causality Exists**
REPO 5YCB1 7.57806 0.0075 Causality Exists*
REPO WPI1 7.25867 0.0088 Causality Exists*
REPO LnER1 5.36949 0.0233 Causality Exists**
REPO RGDP4 2.74704 0.0359 Causality Exists**
REPO NEER1 3.37759 0.0702 Causality Exists***
REPO YR1 1.65235 0.2028 No Causality
REPO T911 0.58853 0.4455 No Causality
REPO T3647 0.27129 0.9625 No Causality
REPO WACR7 0.75522 0.6268 No Causality
REPO CPIINF3 0.04411 0.9876 No Causality
REPO LNNEER1 1.6538 0.2026 No Causality
REPO ZRGDP1 0.14998 0.9793 No Causality
REPO ZIIP1 2.69051 0.1053 No Causality
REPO LNNFC1 0.26532 0.6081 No Causality
REPO ZNFC2 0.31781 0.7288 No Causality
REPO ZTD4 0.81289 0.5217 No Causality
REPO TDR1 0.37235 0.5217 No Causality
*Significant at 1% **Significant at 5 %, *** Significant at 10 % level Annexure 6: Restrictions for SVAR
Table 1: Restriction for SVAR Estimation in Case of Shock in form of Repo Rate
REPO NSE GDP WPI 5YCB 5YGSEC
REPO1 C(5) C(9) C(10) C(12) C(15)
NSE0 1 0 0 C(13) C(16)
GDPC(1) C(6) 1 C(11) 00
WPIC(2) 0 0 100
5YCBC(3) C(7) 0 01 C(17)
5YGSECC(4) C(8) 0 0 C(14) 1
Note: Table 1 shows restriction on SVAR matrix when external shocks are executed in form of repo rare with the above restriction.
Here Variables under consideration are REPO, NSE, GDP, WPI, 5YCB & 5YGSEC to examine policy impact of call money.
Table 2: Restriction for SVAR Estimation in Case of Shock in form of Call Money Rate
WACR NSE GDPWPI5YCB5YGSEC
WACR
1 C(5) C(9) C(10)C(12)C(15)
NSE
0100C(13)C(16)
GDP
C(1) C(6) 1C(11)00
WPI
C(2) 00100
5YCB
C(3) C(7) 001C(17)
5YGSEC
C(4) C(8) 00C(14)1
Note: Table 2 indicates restriction on SVAR matrix when external shocks are imposed in form of call money rate with the above
stated restriction. WACM, NSE, GDP, WPI, 5YCB & 5RGSEC are variables used for defining SVAR to see the policy impact of call
money rate for monetary transmission in India.
Table 3: Restriction for SVAR Estimation in Case of Shock in form of 91Days T-bill Yield 91DAYTBY NSE GDPWPI5YCB5YGSEC
91DAYTBY
1C(5) C(9) C(10)C(12)C(15)
NSE
0100C(13)C(16)
GDP
C(1) C(6) 1C(11)00
WPI
C(2) 00100
5YCB
C(3) C(7) 001C(17)
5YGSEC
C(4) C(8) 00C(14)1
Note: Table 3 specifies restriction on SVAR matrix when external shocks are provided in form of 91Days T-bill Yield with the above
restriction. Variables taken for above SVAR are 91DAYTBY, NSE, GDP, WPI, 5YCB & 5YGSEC to measure impact of 91 days
treasury bill rate on monetary transmission. Annexure 7: SVAR Restrictions
Short-run Restrictions by Pattern Matrices
For many problems, the identifying restrictions on the A and Β matrices are
simple zero exclusion restrictions. In this case, you can specify the
restrictions by creating a named “pattern” matrix for A and Β. Any elements
of the matrix that you want to be estimated should be assigned a missing
value “NA”. All non-missing values in the pattern matrix will be held fxed at
the specifed values.
For example, suppose you want to restrict A to be a lower triangular matrix
with ones on the main diagonal and Β to be a diagonal matrix. Then the
pattern matrices (for a k=3k variable VAR):
A=[
100
NA10
NANA1]
B=[
100
010
001]
Short-run Restrictions in Text Form for Dynamics of Private Investments,
Inflation and GDP
For more general restrictions, you can specify the identifying restrictions in
text form. In-text form, you will write out the relation Ae
t
=Bu
t as a set of
equations, identifying each element of the e
t and u
t vectors with special
symbols. Elements of the A and Β matrices to be estimated must be
specifed as elements of a coefcient vector. Under these restrictions, the
relation Ae
t
=Bu
t can be written as:
e
1
=b
11
u
1
e
2
=−a
21
e
1
+b
22
u
2
e
3
=−a
31
e
1
−a
32
e
2
+b
33
u
3
The restrictions in the text form are as follows:
@e1 = c(1)*@u1
@e2 = -c(2)*@e1 + c(3)*@u2
@e3 = -c(4)*@e1 - c(5)*@e2 + c(6)*@u3 @e4 = -c(7)*@e1 - c(8)*@e2 + c(9)*@u3 + c(10)*@u4
where, @e1 represents REPO residuals, @e2 represents CPI residuals, @e3
represents PCI residuals, @e4 represents GDPGR residuals.
Long-run Restrictions
The identifying restrictions embodied in the relation Ae=Bu are commonly referred to as short-
run restrictions. Blanchard and Quah (1989) proposed an alternative identifcation
method based on restrictions on the long-run properties of the impulse
responses. The (accumulated) long-run response ∁ to structural innovations
takes the form:
∁=
^
Ψ
∞Α
−1
Β
where ^
Ψ
∞=(I−
^
A
1−….−
^
A
p)
−1
is the estimated accumulated responses to the
reduced form (observed) shocks. Long-run identifying restrictions are
specifed in terms of the elements of this ∁ matrix, typically in the form of
zero restrictions. The restriction
C
i,j
=0 means that the (accumulated) response of the ith variable to the jth
structural shock is zero in the long-run.
The expression for the long-run response ∁=
^
Ψ
∞Α
−1
Β involves the inverse of
A. We place all the restrictions linear form in the elements of A and Β, and
the in the long-run restriction, the matrix A is an identity matrix.
To specify long-run restrictions by a pattern matrix, we create a named
matrix that contains the pattern for the long-run response matrix ∁ .
Unrestricted elements in the ∁ matrix should be assigned a missing value
“NA”. For example, suppose you have a k=3k variable VAR where you want
to restrict the long-run response of the second endogenous variable to the
frst structural shock to be zero C
2,1
=0. Then the long-run response matrix will
have the following pattern: C=[
NANA
0NA]
A and Β and are estimated by maximum likelihood, assuming the
innovations are multivariate normal. We evaluate the likelihood in terms of
unconstrained parameters by substituting out the constraints.
Identifcation Condition
The assumption of orthonormal structural innovations imposes k(k+1)/2
restrictions on the 2k
2
unknown elements in A and Β, where k is the number
of endogenous variables in the VAR. To identify A and Β, we provide at least
2k
2
−
k(k+1)
2
=
k(3k−1)
2
additional identifying restrictions. This is a necessary
order condition for identifcation and is checked by counting the number of
restrictions provided.
We have a 4-variable VAR that includes Repo t, CPIt, PCIt, and GDPGRt .
[
u
t
repo
u
t
cpi
u
t
pci
u
t
gdpgr]
=
[
1
b21
b31
b41
0
1
b32
b42
0
0
1
b43
0
0
0
1][
ϵ
t
repo
ϵ
t
cpi
ϵ
t
pci
ϵ
t
gdpgr]
u is the vector of structural innovations and ϵ is the vector of errors from the
reduced form equations where the vector is given by (Repo, CPI, PCI,
GDPGR). Annexure 8: SVAR Impulse Responses
Accumulated Response of GDPGR:Accumulated Response of PCI: Accumulated Response of CPI:
PeriodShock1Shock2Shock3Shock4Shock1Shock2Shock3Shock4Shock1Shock2Shock3Shock4
1 1.76 0.00 0.00 0.00 0.32 1.37 0.00 0.00 0.32-0.522.13 0.00
2 2.80-0.01-0.34-0.100.24 1.12-0.18-0.200.53-1.143.72-0.03
3 3.32 0.49-0.42-0.390.17 1.20-0.12-0.200.42-1.644.81-0.10
4 3.52 0.67-0.39-0.830.15 1.14-0.08-0.200.20-1.975.58-0.19
5 3.55 0.66-0.26-1.220.14 1.11-0.05-0.17-0.03-2.266.10-0.26
6 3.54 0.56-0.11-1.520.14 1.10-0.04-0.15-0.21-2.496.44-0.29
7 3.52 0.45 0.03-1.730.14 1.10-0.03-0.14-0.33-2.656.65-0.29
8 3.50 0.37 0.13-1.880.14 1.10-0.03-0.13-0.41-2.756.76-0.27
9 3.49 0.30 0.20-1.990.13 1.10-0.03-0.12-0.46-2.806.82-0.25
10 3.48 0.26 0.25-2.070.13 1.10-0.03-0.12-0.49-2.836.85-0.22
11 3.47 0.22 0.28-2.140.13 1.10-0.03-0.11-0.50-2.846.86-0.20
12 3.47 0.20 0.30-2.180.13 1.11-0.03-0.11-0.51-2.846.86-0.18
13 3.47 0.18 0.32-2.220.13 1.11-0.03-0.11-0.51-2.846.85-0.17
14 3.47 0.17 0.33-2.240.13 1.11-0.03-0.11-0.51-2.846.85-0.16
15 3.47 0.16 0.33-2.260.13 1.11-0.03-0.10-0.51-2.836.85-0.15
16 3.47 0.16 0.34-2.270.13 1.11-0.03-0.10-0.51-2.836.84-0.14
17 3.47 0.15 0.34-2.280.13 1.11-0.03-0.10-0.51-2.836.84-0.14
18 3.47 0.15 0.34-2.290.13 1.11-0.03-0.10-0.51-2.836.84-0.13
19 3.47 0.15 0.34-2.300.13 1.11-0.04-0.10-0.51-2.836.84-0.13
20 3.47 0.15 0.35-2.300.13 1.11-0.04-0.10-0.51-2.836.84-0.13
Note: Factorization: Structural; Standard Errors: Analytic