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शासन और अनुसंधान
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CLOSURE REPORT
DISCLAIMER
Quantta Analytics Private Limited has received the financial assistance under the Reserac
Scheme of NITI Aayog (RSNA) to prepare this report . While due care has been exercised to
prepare the report using the data from various sources, NITI Aayog does not confirm the
authenticity of data and accuracy of the methodology to prepare the report. NITI Aayog
shall not be held responsible for findings or opinions expressed in the document. This
responsibility completely rests with Quantta Analytics Private Limited.
1. Scope of Work
Quantta was given a project to predict the GVA of India every month before the official
announcements are made. Every month Quantta predicts the YOY and QOQ GVA.
We identified 100 such leading indicators which were reduced to 24 which actually showed
high confidence in predicting the GVA.
2. Our Methodology
A. DATA COLLECTION PROCESS
For predicting the GDP of India every month, Quantta has been collecting sectoral data since
October 2012. Data for the following sectors along with their sources are cited below:
Eight core Industries (Production of Coal in Million Tonnes, Production of Crude Oil in
Thousand Tonnes, Production of Natural Gas in Million cubic meters, Production of
Petroleum Refinery Products in Thousand Tonnes, Production of Fertilizers in Thousand
Tonnes, Production of Steel in Thousand Tonnes, Production of Cement in Thousand
Tonnes) data is being collected from https://eaindustry.nic.in/.
Automobile Sector (Sales of Commercial vehicle and sales of two wheeler) is being
collected from FADA India. In absence of data being present in FADA, or delay in data
published, the same is collected from Business newspaper articles(such as Economic
Times, MoneyControl, etc). For validation, we check % growth/degrowth, of old trailing
month articles.
Energy Sector (Energy requirement and Supply, Energy Peak demand and Peak
Supply) is collected from Central Electricity Authority ; https://cea.nic.in/.
Aviation Sector (Passenger traffic and Airways Freight) is collected from Airport
Authority of India; https://www.aai.aero/. In aviation sector, we take
domestic+international total for both the variables.
Railway Sector (Railway Earnings and railway Freight) is collected from Ministry of
railway Board ; https://indianrailways.gov.in/railwayboard/.
Foreign Trade (Export, Import and service in trade) is collected from data published
on foreign trade by Ministry of Commerce and Industry. NSE Turnover & Market Cap : Is collected from NSE website :
https://www1.nseindia.com/products/content/equities/equities/historical_equity_busi
nessgrowth.htm
Collecting data from official government websites, helps us more accurately predict
the GVA very month.
B. GDP PREDICTING PROCESS:
Since we are studying L.I. and its impact on GVA, we de-trend the series and obtain the
cyclical component of the 20 selected lead indicators using the Hodrick–Prescott (H.P.)
filter. A business cycle describes changes in economic growth as measured by GVA. Each
data series of the all L.I. is converted into logarithm form. We take the Standard Deviation
(SD) of the cyclical component. The log form of the data series is divided by the SD.
Average the standardized series across all components for each quarter to obtain the
Quantta Index. Forecast the trend of L.I. based on the past values. Overlay trend and
impact of GST/demonetization on Quantta Index and calculate Quantta Score y-o-y growth
in Quantta score is predicted y-o-y growth in GVA.
Block Granger Test
A time series of L.I. is said to Granger Cause GVA if the lagged values of L.I. provide
significant information about future values of GVA. Obtain Probability (p) values for each
of the sub indicators. It is used to weigh the strength of the Indicators on GVA. The p-
value is a number between 0 and 1. A small p-value (typically ≤ 0.1) indicates strong
evidence of L.I. impact on GVA. A large p-value (> 0.3) indicates weak evidence/ impact
of L.I. on GVA. A marginal p-value (0.1- 0.3) indicates medium impact of L.I. on GVA. P
values is calculated for each time period. Lowest p value shows highest correlation
between L.I. and GVA.
Products and Services are used in the process of value addition in the Indian Economy.
Depending on the nature of the sector, different sectors contribute to the process of
creating the product or output.
In order to measure the impact of a sector on the economy, we considered time lag,
measured as T Minus from the period under study. For example, fertilizer is a sector that
indicates the sentiment of the farmer to grow crops. The consumption of fertilizers show
a narrow time lag between consumption and its impact on the economy making it a lead
indicator about the state of the economy. The domestic passenger traffic on the other
hand shows a significant lag indicating that people postpone travel when uncertainty
increases and increase their travel when they have a positive outlook on the historic
performance of their business and the econom y. These signals are vital to understand
the underlying trends in the economy.
3. Y-O-Y GROWTH ACTUAL VERSUS PREDICTED
Quantta has been able to closely mirror the actual outcome. We have been stress
testing over 32 quarters. Some of the deviations have been because of the structural
changes such as effect of Demonitization/ VAT etc.
Quarters
Predicted Growth
in GVA
Actual Growth in GVA
Q1_FY15 8.19%
7.75%
Q2_FY15 8.30%
8.45%
Q3_FY15 6.43%
6.14%
Q4_FY15 6.53%
6.39%
Q1_FY16 7.55%
7.70%
Q2_FY16 7.75%
8.36%
Q3_FY16 7.51%
7.33%
Q4_FY16 9.25%
8.72%
Q1_FY17 8.34%
9.32%
Q2_FY17 9.02%
8.29%
Q3_FY17 7.80%
7.53%
Q4_FY17 6.18%
6.83%
Q1_FY18 6.09%
5.48%
Q2_FY18 5.94%
6.11%
Q3_FY18 6.60%
7.07%
Q4_FY18 7.66%
7.63%
Q1_FY19 7.49%
6.95%
Q2_FY19 5.75%
6.06%
Q3_FY19 5.82%
5.62%
Q4_FY19 5.46%
5.55%
Q1_FY20 4.55%
4.76%
Q2_FY20 4.81%
4.33% Q3_FY20 3.31%
3.47%
Q4_FY20 2.98%
3.04%
Q1_FY21 -22.89%
-22.81%
Q2_FY21 -7.70%
-7.00%
Q3_FY21 0.73%
0.58%
Q4_FY21 6.58%
3.50%
Q1_FY22 45.52%
19.36%
Q2_FY22 27.07%
7.88%
Q3_FY22 15.46%
Q4_FY22 8.43%
-30.00%
-20.00%
-10.00%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
Growth
Quarters
Pred_growth_GVA actual_growth_GVA actual_growth_GDP
DISCLAIMER
Quantta Analytics Private Limited has received the financial assistance under the Reserac
Scheme of NITI Aayog (RSNA) to prepare this report . While due care has been exercised to
prepare the report using the data from various sources, NITI Aayog does not confirm the
authenticity of data and accuracy of the methodology to prepare the report. NITI Aayog
shall not be held responsible for findings or opinions expressed in the document. This
responsibility completely rests with Quantta Analytics Private Limited.
1. Scope of Work
Quantta was given a project to predict the GVA of India every month before the official
announcements are made. Every month Quantta predicts the YOY and QOQ GVA.
We identified 100 such leading indicators which were reduced to 24 which actually showed
high confidence in predicting the GVA.
2. Our Methodology
A. DATA COLLECTION PROCESS
For predicting the GDP of India every month, Quantta has been collecting sectoral data since
October 2012. Data for the following sectors along with their sources are cited below:
Eight core Industries (Production of Coal in Million Tonnes, Production of Crude Oil in
Thousand Tonnes, Production of Natural Gas in Million cubic meters, Production of
Petroleum Refinery Products in Thousand Tonnes, Production of Fertilizers in Thousand
Tonnes, Production of Steel in Thousand Tonnes, Production of Cement in Thousand
Tonnes) data is being collected from https://eaindustry.nic.in/.
Automobile Sector (Sales of Commercial vehicle and sales of two wheeler) is being
collected from FADA India. In absence of data being present in FADA, or delay in data
published, the same is collected from Business newspaper articles(such as Economic
Times, MoneyControl, etc). For validation, we check % growth/degrowth, of old trailing
month articles.
Energy Sector (Energy requirement and Supply, Energy Peak demand and Peak
Supply) is collected from Central Electricity Authority ; https://cea.nic.in/.
Aviation Sector (Passenger traffic and Airways Freight) is collected from Airport
Authority of India; https://www.aai.aero/. In aviation sector, we take
domestic+international total for both the variables.
Railway Sector (Railway Earnings and railway Freight) is collected from Ministry of
railway Board ; https://indianrailways.gov.in/railwayboard/.
Foreign Trade (Export, Import and service in trade) is collected from data published
on foreign trade by Ministry of Commerce and Industry. NSE Turnover & Market Cap : Is collected from NSE website :
https://www1.nseindia.com/products/content/equities/equities/historical_equity_busi
nessgrowth.htm
Collecting data from official government websites, helps us more accurately predict
the GVA very month.
B. GDP PREDICTING PROCESS:
Since we are studying L.I. and its impact on GVA, we de-trend the series and obtain the
cyclical component of the 20 selected lead indicators using the Hodrick–Prescott (H.P.)
filter. A business cycle describes changes in economic growth as measured by GVA. Each
data series of the all L.I. is converted into logarithm form. We take the Standard Deviation
(SD) of the cyclical component. The log form of the data series is divided by the SD.
Average the standardized series across all components for each quarter to obtain the
Quantta Index. Forecast the trend of L.I. based on the past values. Overlay trend and
impact of GST/demonetization on Quantta Index and calculate Quantta Score y-o-y growth
in Quantta score is predicted y-o-y growth in GVA.
Block Granger Test
A time series of L.I. is said to Granger Cause GVA if the lagged values of L.I. provide
significant information about future values of GVA. Obtain Probability (p) values for each
of the sub indicators. It is used to weigh the strength of the Indicators on GVA. The p-
value is a number between 0 and 1. A small p-value (typically ≤ 0.1) indicates strong
evidence of L.I. impact on GVA. A large p-value (> 0.3) indicates weak evidence/ impact
of L.I. on GVA. A marginal p-value (0.1- 0.3) indicates medium impact of L.I. on GVA. P
values is calculated for each time period. Lowest p value shows highest correlation
between L.I. and GVA.
Products and Services are used in the process of value addition in the Indian Economy.
Depending on the nature of the sector, different sectors contribute to the process of
creating the product or output.
In order to measure the impact of a sector on the economy, we considered time lag,
measured as T Minus from the period under study. For example, fertilizer is a sector that
indicates the sentiment of the farmer to grow crops. The consumption of fertilizers show
a narrow time lag between consumption and its impact on the economy making it a lead
indicator about the state of the economy. The domestic passenger traffic on the other
hand shows a significant lag indicating that people postpone travel when uncertainty
increases and increase their travel when they have a positive outlook on the historic
performance of their business and the econom y. These signals are vital to understand
the underlying trends in the economy.
3. Y-O-Y GROWTH ACTUAL VERSUS PREDICTED
Quantta has been able to closely mirror the actual outcome. We have been stress
testing over 32 quarters. Some of the deviations have been because of the structural
changes such as effect of Demonitization/ VAT etc.
Quarters
Predicted Growth
in GVA
Actual Growth in GVA
Q1_FY15 8.19%
7.75%
Q2_FY15 8.30%
8.45%
Q3_FY15 6.43%
6.14%
Q4_FY15 6.53%
6.39%
Q1_FY16 7.55%
7.70%
Q2_FY16 7.75%
8.36%
Q3_FY16 7.51%
7.33%
Q4_FY16 9.25%
8.72%
Q1_FY17 8.34%
9.32%
Q2_FY17 9.02%
8.29%
Q3_FY17 7.80%
7.53%
Q4_FY17 6.18%
6.83%
Q1_FY18 6.09%
5.48%
Q2_FY18 5.94%
6.11%
Q3_FY18 6.60%
7.07%
Q4_FY18 7.66%
7.63%
Q1_FY19 7.49%
6.95%
Q2_FY19 5.75%
6.06%
Q3_FY19 5.82%
5.62%
Q4_FY19 5.46%
5.55%
Q1_FY20 4.55%
4.76%
Q2_FY20 4.81%
4.33% Q3_FY20 3.31%
3.47%
Q4_FY20 2.98%
3.04%
Q1_FY21 -22.89%
-22.81%
Q2_FY21 -7.70%
-7.00%
Q3_FY21 0.73%
0.58%
Q4_FY21 6.58%
3.50%
Q1_FY22 45.52%
19.36%
Q2_FY22 27.07%
7.88%
Q3_FY22 15.46%
Q4_FY22 8.43%
-30.00%
-20.00%
-10.00%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
Growth
Quarters
Pred_growth_GVA actual_growth_GVA actual_growth_GDP