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उद्योग एवं विदेशी निवेश
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GVA Prediction India
Acknowledgement
The study was sponsored with financial support of
NITI Aayog, Government of India and conducted
by Quantta Analytics Pvt. Ltd, Kolkata. Quanttawasgivenaprojecttoidentify
leadingindicatorsthatprovidedanearly
warningsystemofthedirectionand
changeoftheGVAandGDP.
Weidentified~100potentialindicators
whichwasfinallyreducedto~24that
showedhighconfidenceinpredicting
thedirectionofeconomygrowth
SCOPE OF PROJECT DISCLAIMER
QuanttaAnalyticsPvt.Ltd,hasreceivedthegrantundertheResearchSchemeofNITIAayog,2018toproducethedocument.
However,NITIAayogshallnotbeheldresponsibleforfindingsoropinionsexpressedinthedocumentprepared.Thisresponsibility
restswithQuanttaAnalyticsPvt.Ltd. SUMMARY
WE PREDICT
•Positive 9.93%Growth in GVA for Q3 FY 2021-22
•Positive 8.80%Growth in GVA for Q4 FY 2021-22 Quick
Summary Key Findings
Withthenormalizationoflivelihood,weareseeing
improvementandpositivesignsinallsectors.
Passengertrafficofairwaysandrailwaysare
increasingeverydayasmorepeoplearenow
travellingforworkaswellasleisure.
Theexportandimportsectorisshowingastrong
recovery.
Theeightcoresectorisalsoshowingagood
recovery,thesteel,cement,crudeallsectorsare
showinganupwardtrendinggraph.
OveralltheIndianeconomyisdoinggood. Variables Identified For Further Testing
We looked at the following seven variables for predicting the GVA
accurately:
•Monthly turnover at NSE
•Market capitalization of BSE
•Monthly turnover at BSE
•Market capitalization of NSE
•Monthly average price of gold
•Monthly average price of silver
•USD INR exchange rate
•Out of the above eight variables, the following four variables gave us
good prediction power and we incorporated the same:
•Monthly turnover at NSE
•Market capitalization of NSE
•Monthly average price of gold
•USD INR exchange rate
For NSE, we are using total turnover as of now. Instead of turnover,
we can use its components: price and volume.To get price, we can
choose either one popular index or stock that can be representative
of whole Indian market. Y-O-Y Growth
Actual Versus
Predicted
-30.00%
-20.00%
-10.00%
0.00%
10.00%
20.00%
30.00%
40.00%
Q3_FY14 Q1_FY15 Q3_FY15 Q1_FY16 Q3_FY16 Q1_FY17 Q3_FY17 Q1_FY18 Q3_FY18 Q1_FY19 Q3_FY19 Q1_FY20 Q3_FY20 Q1_FY21 Q3_FY21 Q1_FY22 Q3_FY22
Growth
Quarters
Pred_growth_GVA actual_growth_GVA actual_growth_GDP Q-O-Q Growth
Actual Versus
Predicted
-40.00%
-30.00%
-20.00%
-10.00%
0.00%
10.00%
20.00%
30.00%
Q3_FY14 Q1_FY15 Q3_FY15 Q1_FY16 Q3_FY16 Q1_FY17 Q3_FY17 Q1_FY18 Q3_FY18 Q1_FY19 Q3_FY19 Q1_FY20 Q3_FY20 Q1_FY21 Q3_FY21 Q1_FY22 Q3_FY22
Growth
Quarters
Pred_growth_GVA actual_growth_GVA actual_growth_GDP 3.03%
Q2_FY17
0.69%
-0.24%
Q3_FY17
1.73%
1.56%
Q4_FY17
1.93%
2.34%
Q1_FY18
1.61%
1.73%
Q2_FY18
0.55%
0.35%
Q3_FY18
2.37%
2.48%
Q4_FY18
2.94%
2.87%
Q1_FY19
1.45%
1.09%
Q2_FY19
-1.08%
Y-O-Y Growth Actual
Versus Predicted
Our predicted growth in GVA has
closely mirrored the actual outcome.
We have stress tested this over the
past eight years. We believe that this
provides a good early warning system
for the likely change in GVA in a
quarter. Some of the deviations are
also because of structural changes
such as Demonetization or VAT.
Quarters PredictedGrowth in GVA Actual Growth in GVA
Q1_FY158.19%7.75%
Q2_FY158.30%8.45%
Q3_FY156.43%6.14%
Q4_FY156.53%6.39%
Q1_FY167.55%7.70%
Q2_FY167.75%8.36%
Q3_FY167.51%7.33%
Q4_FY169.25%8.72%
Q1_FY178.34%9.32%
Q2_FY179.02%8.29%
Q3_FY177.80%7.53%
Q4_FY176.18%6.83%
Q1_FY186.09%5.48%
Q2_FY185.94%6.11%
Q3_FY186.60%7.07%
Q4_FY187.66%7.63%
Q1_FY197.49%6.95%
Q2_FY195.75%6.06%
Q3_FY195.82%5.62%
Q4_FY195.46%5.55%
Q1_FY204.55%4.76%
Q2_FY204.81%4.33%
Q3_FY203.31%3.47%
Q4_FY202.98%3.04%
Q1_FY21-22.89%-22.81%
Q2_FY21-7.70%-7.00%
Q3_FY210.73%0.58%
Q4_FY212.80%3.50%
Q1_FY2228.49%19.36%
Q2_FY2218.15%7.88%
Q3_FY229.93%
Q4_FY228.80% Q-O-Q Growth Actual
Versus Predicted
Our predicted growth in GVA has
closely mirrored the actual outcome.
We have stress tested this over the
past thirty two quarters. We believe
that this provides a good early
warning system for the likely change
in GVA in a quarter. Some of the
deviations are also because of
structural changes such as
Demonetization or VAT.
Quarters PredictedGrowth in GVA Actual Growth in GVA
Q1_FY151.57%1.22%
Q2_FY15-0.12%0.09%
Q3_FY153.12%3.26%
Q4_FY151.83%1.69%
Q1_FY162.55%2.46%
Q2_FY160.07%0.70%
Q3_FY162.88%2.28%
Q4_FY163.48%3.01%
Q1_FY171.69%3.03%
Q2_FY170.69%-0.24%
Q3_FY171.73%1.56%
Q4_FY171.93%2.34%
Q1_FY181.61%1.73%
Q2_FY180.55%0.35%
Q3_FY182.37%2.48%
Q4_FY182.94%2.87%
Q1_FY191.45%1.09%
Q2_FY19-1.08%-0.49%
Q3_FY192.43%2.07%
Q4_FY192.59%2.80%
Q1_FY200.57%0.33%
Q2_FY20-0.83%-0.89%
Q3_FY200.96%1.22%
Q4_FY202.27%2.38%
Q1_FY21-24.69%-24.84%
Q2_FY2118.71%19.40%
Q3_FY2110.18%9.48%
Q4_FY214.37%5.34%
Q1_FY22-5.88%-13.32%
Q2_FY229.16%7.92%
Q3_FY222.51%
Q4_FY223.30% AUTOMOBILE –
COMMERCIAL & TWO WHEELER Commercial
Vehicle Sale
Volume with
Prediction
0
20000
40000
60000
80000
100000
120000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Sales ( in units )
Actual Forecast Two Wheeler
Vehicle Sale
Volume with
Prediction
0
500000
1000000
1500000
2000000
2500000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Sales ( in units)
ActualForecast AIRLINE TRAFFIC –
PASSENGER & FREIGHT Airline
Passenger
Traffic with
Prediction
0
5000000
10000000
15000000
20000000
25000000
30000000
35000000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Passenger Traffic
ActualForecast Airline Freight
Volume with
Prediction
0
50000
100000
150000
200000
250000
300000
350000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Airline freight ( in tonnes )
ActualForecast RAILWAY –
FREIGHT & EARNINGS Railway
Earning with
Prediction
0
5000
10000
15000
20000
25000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Earnings ( in crores )
ActualForecast Railway
Freight Traffic
with
Prediction
0
20
40
60
80
100
120
140
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Railway freight ( in million tonnes )
Actual Forecast POWER
DEMAND & SUPPLY Energy
Requirement
in Mega Units
0
20000
40000
60000
80000
100000
120000
140000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Energy requirement ( in mega units )
ActualForecast Energy
Supplied in
Mega Units
0
20000
40000
60000
80000
100000
120000
140000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Energy Supplied ( in mega units )
ActualForecast Energy
Peak
Demand
0
50000
100000
150000
200000
250000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
energy peak demand ( in mega units )
Actual Forecast Energy
Peak
Supply
0
50000
100000
150000
200000
250000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
energy peak supply ( in mega units )
Actual Forecast MANUFACTURING Coal Production
in Million
Tonnes with
Prediction
0
20
40
60
80
100
120
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Production ( in million tonnes )
Actual Forecast Petroleum
Production in
Tonnes with
Prediction
0
5000
10000
15000
20000
25000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Production ( in dollar thousand
tonnes )
Actual Forecast Natural Gas
Production with
Prediction
0
500
1000
1500
2000
2500
3000
3500
4000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Production ( in million cubic meters )
ActualForecast Crude Oil
Production with
Prediction
0
500
1000
1500
2000
2500
3000
3500
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Production ( in thousand tonnes )
Actual Forecast Fertilizer
Production with
Prediction
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Production ( in thousand tonnes )
ActualForecast Cement
Production with
Prediction
0
5000
10000
15000
20000
25000
30000
35000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Production ( in thousand tonnes )
ActualForecast Steel
Production with
Prediction
0
2000
4000
6000
8000
10000
12000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Production ( in thousand tonnes )
Actual Forecast IMPORT & EXPORT –
GOODS & SERVICES Export with
Prediction
0
50000
100000
150000
200000
250000
300000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Export ( in Rs. Crores )
Actual Forecast Import
with
Prediction
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
500000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Imports ( in Rs. Crores )
Actual Forecast Trade in
Service with
Prediction
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
trade in services Actual Forecast USD INR
exchange
rate
56
58
60
62
64
66
68
70
72
74
76
78
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Average exchange rate ActualForecast COMMODITY –GOLD Average price
of gold with
Prediction
0
10000
20000
30000
40000
50000
60000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Average price of gold
Actual Forecast STOCK MARKET –TURNOVER
AND MARKET CAPITALISATION Turnover at
NSE with
prediction
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Turnover at NSE
Actual Forecast Market
Capitalization of
NSE with
Prediction
0
5000000
10000000
15000000
20000000
25000000
30000000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Market capitalisation at NSE
Actual Forecast QUANTTA MODEL AUTOMOBILE
AVIATION
COMMODITY
STOCK MARKETS
BANKING SECTOR
RAILWAYS
POWER
MANUFACTURING SECTOR
EXPORT & IMPORT OF GOODS & SERVICES
Sectors Selected for Analysis IndicatorsLag1 Lag2 Lag3 Lag4
Commercial Vehicles Sales0.085 0.100 0.004 0.239
TwoWheelers Sales0.193 0.534 0.932 0.430
ProductionofCoalinMillionTonnes0.812 0.078 0.091 0.490
ProductionofCrudeOilinThousandTonnes0.407 0.033 0.001 0.006
ProductionofNaturalGasinMillioncubicmeters0.005 0.004 0.003 0.113
ProductionofPetroleumRefineryProductsDollarinThousandTonnes0.046 0.040 0.271 0.484
ProductionofFertilizersinThousandTonnes0.000 0.060 0.152 0.582
ProductionofSteelinThousandTonnes0.130 0.838 0.542 0.825
Productionof CementinThousandTonnes0.001 0.044 0.171 0.055
EnergyRequirementMU0.302 0.554 0.391 0.059
EnergySuppliedMU0.146 0.648 0.361 0.068
EnergyPeakDemandMU0.531 0.596 0.707 0.310
EnergyPeakSupplyMU0.233 0.891 0.778 0.407
RailwayFreightTonnageinmilliontonnes0.044 0.001 0.021 0.133
Indicators Used for Analysis with Time Lag IndicatorsLag 1 Lag 2 Lag 3 Lag 4
Railways Passengers (millions)0.9590.121 0.290 0.535
Railways Earnings (Crores)0.024 0.002 0.029 0.405
Airline Passengers0.329 0.000 0.000 0.002
Airline Freight0.232 0.279 0.575 0.350
Exports of Goods (Crores)0.733 0.260 0.140 0.725
Import of Goods (Crores)0.511 0.556 0.675 0.338
Export of Services0.328 0.084 0.387 0.147
Import of Services0.491 0.967 0.245 0.385
Statutory Liquidity Ratio0.075 0.192 0.637 0.185
Cash Deposit Ratio0.004 0.006 0.361 0.121
Credit Deposit Ratio0.019 0.053 0.681 0.600
Policy Repo Rate0.176 0.006 0.182 0.782
Reverse Repo Rate0.625 0.129 0.432 0.721
Marginal_Standing_Facility_Rate0.458 0.005 0.013 0.040
Bank_Rate0.458 0.005 0.013 0.040
NSE Turnover0.4600.308 0.555 0.360
NSE Index (Moving Average)0.691 0.476 0.866 0.922
Indicators Used for Analysis with Time Lag IndicatorsQuarterly Time Lag
CommercialVehiclesSalesT-3
TwoWheelers SalesT-0
AirlinePassengers DomesticT-3
AirlineFreight
T-0
RailwaysFreight
T-2
Railways Earnings (Crores)
T-2
EnergyRequirementMUT-3
EnergySuppliedMUT-3
EnergyPeakDemandMUT-0
EnergyPeakSupplyMUT-0
ProductionofCoalinMillionTonnesT-2
ProductionofPetroleumRefineryProductsDollarinThousandTonnesT-2
ProductionofNaturalGasinMillioncubicmetersT-3
ProductionofCrudeOilinThousandTonnesT-3
ProductionofFertilizersinThousandTonnesT-1
Productionof CementinThousandTonnesT-1
ProductionofSteelinThousandTonnesT-0
Exportsof Goods (Crores)
T-0
Importof Goods (Crores)
T-0
Export of Services
USD INR exchange rate
Market capitalization and turnover of NSE
Monthly average price of gold
T-2
T-0
T-0
T-1
Final Lead Indicators Selected Methodology
SincewearestudyingL.I.anditsimpactonGVA,wede-trendtheseries
andobtainthecyclicalcomponent ofthe20selectedlead
indicatorsusingtheHodrick–Prescott(H.P.)filter.Abusinesscycle
describeschangesineconomicgrowthasmeasuredbyGVA.Each
dataseriesoftheallL.I.isconvertedintologarithmform.Wetakethe
StandardDeviation(SD)ofthecyclicalcomponent.Thelogformofthe
dataseriesisdividedbytheSD.Averagethestandardizedseriesacross
allcomponentsforeachquartertoobtaintheQuanttaIndex.Forecast
thetrendofL.I.basedonthepastvalues.Overlaytrendandimpactof
GST/demonetizationonQuanttaIndexandcalculateQuanttaScorey-
o-ygrowthinQuanttascoreispredictedy-o-ygrowthinGVA
Block Granger Test
AtimeseriesofL.I.issaidtoGrangerCauseGVAifthelaggedvaluesof
L.I.providesignificantinformationaboutfuturevaluesofGVA.Obtain
Probability(p)valuesforeachofthesubindicators.Itisusedtoweigh
thestrengthoftheIndicatorsonGVA.Thep-valueisanumberbetween
0and1.Asmallp-value(typically≤0.1)indicatesstrongevidenceofL.I.
impactonGVA.Alargep-value(>0.3)indicatesweakevidence/
impactofL.I.onGVA.Amarginalp-value(0.1-0.3)indicatesmedium
impactofL.I.onGVA.Pvaluesiscalculatedforeachtimeperiod.
LowestpvalueshowshighestcorrelationbetweenL.I.andGVA
ProductsandServicesareusedintheprocessofvalueadditioninthe
Economy.Dependingonthenatureofthesector,differentsectors
theprocessofcreatingtheproductoroutput.
Inordertomeasuretheimpactofasectorontheeconomy,we
lag,measuredasTMinusfromtheperiodunderstudy.Forexample,
asectorthatindicatesthesentimentofthefarmertogrowcrops.The
consumptionoffertilizersshowanarrowtimelagbetweenconsumption
impactontheeconomymakingitaleadindicatoraboutthestateof
economy.Thedomesticpassengertrafficontheotherhandshowsa
lagindicatingthatpeoplepostponetravelwhenuncertaintyincreases
increasetheirtravelwhentheyhaveapositiveoutlookonthehistoric
performanceoftheirbusinessandtheeconomy.Thesesignalsarevital
understandtheunderlyingtrendsintheeconomy.
Acknowledgement
The study was sponsored with financial support of
NITI Aayog, Government of India and conducted
by Quantta Analytics Pvt. Ltd, Kolkata. Quanttawasgivenaprojecttoidentify
leadingindicatorsthatprovidedanearly
warningsystemofthedirectionand
changeoftheGVAandGDP.
Weidentified~100potentialindicators
whichwasfinallyreducedto~24that
showedhighconfidenceinpredicting
thedirectionofeconomygrowth
SCOPE OF PROJECT DISCLAIMER
QuanttaAnalyticsPvt.Ltd,hasreceivedthegrantundertheResearchSchemeofNITIAayog,2018toproducethedocument.
However,NITIAayogshallnotbeheldresponsibleforfindingsoropinionsexpressedinthedocumentprepared.Thisresponsibility
restswithQuanttaAnalyticsPvt.Ltd. SUMMARY
WE PREDICT
•Positive 9.93%Growth in GVA for Q3 FY 2021-22
•Positive 8.80%Growth in GVA for Q4 FY 2021-22 Quick
Summary Key Findings
Withthenormalizationoflivelihood,weareseeing
improvementandpositivesignsinallsectors.
Passengertrafficofairwaysandrailwaysare
increasingeverydayasmorepeoplearenow
travellingforworkaswellasleisure.
Theexportandimportsectorisshowingastrong
recovery.
Theeightcoresectorisalsoshowingagood
recovery,thesteel,cement,crudeallsectorsare
showinganupwardtrendinggraph.
OveralltheIndianeconomyisdoinggood. Variables Identified For Further Testing
We looked at the following seven variables for predicting the GVA
accurately:
•Monthly turnover at NSE
•Market capitalization of BSE
•Monthly turnover at BSE
•Market capitalization of NSE
•Monthly average price of gold
•Monthly average price of silver
•USD INR exchange rate
•Out of the above eight variables, the following four variables gave us
good prediction power and we incorporated the same:
•Monthly turnover at NSE
•Market capitalization of NSE
•Monthly average price of gold
•USD INR exchange rate
For NSE, we are using total turnover as of now. Instead of turnover,
we can use its components: price and volume.To get price, we can
choose either one popular index or stock that can be representative
of whole Indian market. Y-O-Y Growth
Actual Versus
Predicted
-30.00%
-20.00%
-10.00%
0.00%
10.00%
20.00%
30.00%
40.00%
Q3_FY14 Q1_FY15 Q3_FY15 Q1_FY16 Q3_FY16 Q1_FY17 Q3_FY17 Q1_FY18 Q3_FY18 Q1_FY19 Q3_FY19 Q1_FY20 Q3_FY20 Q1_FY21 Q3_FY21 Q1_FY22 Q3_FY22
Growth
Quarters
Pred_growth_GVA actual_growth_GVA actual_growth_GDP Q-O-Q Growth
Actual Versus
Predicted
-40.00%
-30.00%
-20.00%
-10.00%
0.00%
10.00%
20.00%
30.00%
Q3_FY14 Q1_FY15 Q3_FY15 Q1_FY16 Q3_FY16 Q1_FY17 Q3_FY17 Q1_FY18 Q3_FY18 Q1_FY19 Q3_FY19 Q1_FY20 Q3_FY20 Q1_FY21 Q3_FY21 Q1_FY22 Q3_FY22
Growth
Quarters
Pred_growth_GVA actual_growth_GVA actual_growth_GDP 3.03%
Q2_FY17
0.69%
-0.24%
Q3_FY17
1.73%
1.56%
Q4_FY17
1.93%
2.34%
Q1_FY18
1.61%
1.73%
Q2_FY18
0.55%
0.35%
Q3_FY18
2.37%
2.48%
Q4_FY18
2.94%
2.87%
Q1_FY19
1.45%
1.09%
Q2_FY19
-1.08%
Y-O-Y Growth Actual
Versus Predicted
Our predicted growth in GVA has
closely mirrored the actual outcome.
We have stress tested this over the
past eight years. We believe that this
provides a good early warning system
for the likely change in GVA in a
quarter. Some of the deviations are
also because of structural changes
such as Demonetization or VAT.
Quarters PredictedGrowth in GVA Actual Growth in GVA
Q1_FY158.19%7.75%
Q2_FY158.30%8.45%
Q3_FY156.43%6.14%
Q4_FY156.53%6.39%
Q1_FY167.55%7.70%
Q2_FY167.75%8.36%
Q3_FY167.51%7.33%
Q4_FY169.25%8.72%
Q1_FY178.34%9.32%
Q2_FY179.02%8.29%
Q3_FY177.80%7.53%
Q4_FY176.18%6.83%
Q1_FY186.09%5.48%
Q2_FY185.94%6.11%
Q3_FY186.60%7.07%
Q4_FY187.66%7.63%
Q1_FY197.49%6.95%
Q2_FY195.75%6.06%
Q3_FY195.82%5.62%
Q4_FY195.46%5.55%
Q1_FY204.55%4.76%
Q2_FY204.81%4.33%
Q3_FY203.31%3.47%
Q4_FY202.98%3.04%
Q1_FY21-22.89%-22.81%
Q2_FY21-7.70%-7.00%
Q3_FY210.73%0.58%
Q4_FY212.80%3.50%
Q1_FY2228.49%19.36%
Q2_FY2218.15%7.88%
Q3_FY229.93%
Q4_FY228.80% Q-O-Q Growth Actual
Versus Predicted
Our predicted growth in GVA has
closely mirrored the actual outcome.
We have stress tested this over the
past thirty two quarters. We believe
that this provides a good early
warning system for the likely change
in GVA in a quarter. Some of the
deviations are also because of
structural changes such as
Demonetization or VAT.
Quarters PredictedGrowth in GVA Actual Growth in GVA
Q1_FY151.57%1.22%
Q2_FY15-0.12%0.09%
Q3_FY153.12%3.26%
Q4_FY151.83%1.69%
Q1_FY162.55%2.46%
Q2_FY160.07%0.70%
Q3_FY162.88%2.28%
Q4_FY163.48%3.01%
Q1_FY171.69%3.03%
Q2_FY170.69%-0.24%
Q3_FY171.73%1.56%
Q4_FY171.93%2.34%
Q1_FY181.61%1.73%
Q2_FY180.55%0.35%
Q3_FY182.37%2.48%
Q4_FY182.94%2.87%
Q1_FY191.45%1.09%
Q2_FY19-1.08%-0.49%
Q3_FY192.43%2.07%
Q4_FY192.59%2.80%
Q1_FY200.57%0.33%
Q2_FY20-0.83%-0.89%
Q3_FY200.96%1.22%
Q4_FY202.27%2.38%
Q1_FY21-24.69%-24.84%
Q2_FY2118.71%19.40%
Q3_FY2110.18%9.48%
Q4_FY214.37%5.34%
Q1_FY22-5.88%-13.32%
Q2_FY229.16%7.92%
Q3_FY222.51%
Q4_FY223.30% AUTOMOBILE –
COMMERCIAL & TWO WHEELER Commercial
Vehicle Sale
Volume with
Prediction
0
20000
40000
60000
80000
100000
120000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Sales ( in units )
Actual Forecast Two Wheeler
Vehicle Sale
Volume with
Prediction
0
500000
1000000
1500000
2000000
2500000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Sales ( in units)
ActualForecast AIRLINE TRAFFIC –
PASSENGER & FREIGHT Airline
Passenger
Traffic with
Prediction
0
5000000
10000000
15000000
20000000
25000000
30000000
35000000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Passenger Traffic
ActualForecast Airline Freight
Volume with
Prediction
0
50000
100000
150000
200000
250000
300000
350000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Airline freight ( in tonnes )
ActualForecast RAILWAY –
FREIGHT & EARNINGS Railway
Earning with
Prediction
0
5000
10000
15000
20000
25000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Earnings ( in crores )
ActualForecast Railway
Freight Traffic
with
Prediction
0
20
40
60
80
100
120
140
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Railway freight ( in million tonnes )
Actual Forecast POWER
DEMAND & SUPPLY Energy
Requirement
in Mega Units
0
20000
40000
60000
80000
100000
120000
140000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Energy requirement ( in mega units )
ActualForecast Energy
Supplied in
Mega Units
0
20000
40000
60000
80000
100000
120000
140000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Energy Supplied ( in mega units )
ActualForecast Energy
Peak
Demand
0
50000
100000
150000
200000
250000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
energy peak demand ( in mega units )
Actual Forecast Energy
Peak
Supply
0
50000
100000
150000
200000
250000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
energy peak supply ( in mega units )
Actual Forecast MANUFACTURING Coal Production
in Million
Tonnes with
Prediction
0
20
40
60
80
100
120
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Production ( in million tonnes )
Actual Forecast Petroleum
Production in
Tonnes with
Prediction
0
5000
10000
15000
20000
25000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Production ( in dollar thousand
tonnes )
Actual Forecast Natural Gas
Production with
Prediction
0
500
1000
1500
2000
2500
3000
3500
4000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Production ( in million cubic meters )
ActualForecast Crude Oil
Production with
Prediction
0
500
1000
1500
2000
2500
3000
3500
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Production ( in thousand tonnes )
Actual Forecast Fertilizer
Production with
Prediction
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Production ( in thousand tonnes )
ActualForecast Cement
Production with
Prediction
0
5000
10000
15000
20000
25000
30000
35000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Production ( in thousand tonnes )
ActualForecast Steel
Production with
Prediction
0
2000
4000
6000
8000
10000
12000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Production ( in thousand tonnes )
Actual Forecast IMPORT & EXPORT –
GOODS & SERVICES Export with
Prediction
0
50000
100000
150000
200000
250000
300000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Export ( in Rs. Crores )
Actual Forecast Import
with
Prediction
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
500000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Imports ( in Rs. Crores )
Actual Forecast Trade in
Service with
Prediction
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
trade in services Actual Forecast USD INR
exchange
rate
56
58
60
62
64
66
68
70
72
74
76
78
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Average exchange rate ActualForecast COMMODITY –GOLD Average price
of gold with
Prediction
0
10000
20000
30000
40000
50000
60000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Average price of gold
Actual Forecast STOCK MARKET –TURNOVER
AND MARKET CAPITALISATION Turnover at
NSE with
prediction
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Turnover at NSE
Actual Forecast Market
Capitalization of
NSE with
Prediction
0
5000000
10000000
15000000
20000000
25000000
30000000
Apr-16
Jul-16
Oct-16 Jan-17 Apr-17
Jul-17
Oct-17 Jan-18 Apr-18
Jul-18
Oct-18 Jan-19 Apr-19
Jul-19
Oct-19 Jan-20 Apr-20
Jul-20
Oct-20 Jan-21 Apr-21
Jul-21
Oct-21 Jan-22
Market capitalisation at NSE
Actual Forecast QUANTTA MODEL AUTOMOBILE
AVIATION
COMMODITY
STOCK MARKETS
BANKING SECTOR
RAILWAYS
POWER
MANUFACTURING SECTOR
EXPORT & IMPORT OF GOODS & SERVICES
Sectors Selected for Analysis IndicatorsLag1 Lag2 Lag3 Lag4
Commercial Vehicles Sales0.085 0.100 0.004 0.239
TwoWheelers Sales0.193 0.534 0.932 0.430
ProductionofCoalinMillionTonnes0.812 0.078 0.091 0.490
ProductionofCrudeOilinThousandTonnes0.407 0.033 0.001 0.006
ProductionofNaturalGasinMillioncubicmeters0.005 0.004 0.003 0.113
ProductionofPetroleumRefineryProductsDollarinThousandTonnes0.046 0.040 0.271 0.484
ProductionofFertilizersinThousandTonnes0.000 0.060 0.152 0.582
ProductionofSteelinThousandTonnes0.130 0.838 0.542 0.825
Productionof CementinThousandTonnes0.001 0.044 0.171 0.055
EnergyRequirementMU0.302 0.554 0.391 0.059
EnergySuppliedMU0.146 0.648 0.361 0.068
EnergyPeakDemandMU0.531 0.596 0.707 0.310
EnergyPeakSupplyMU0.233 0.891 0.778 0.407
RailwayFreightTonnageinmilliontonnes0.044 0.001 0.021 0.133
Indicators Used for Analysis with Time Lag IndicatorsLag 1 Lag 2 Lag 3 Lag 4
Railways Passengers (millions)0.9590.121 0.290 0.535
Railways Earnings (Crores)0.024 0.002 0.029 0.405
Airline Passengers0.329 0.000 0.000 0.002
Airline Freight0.232 0.279 0.575 0.350
Exports of Goods (Crores)0.733 0.260 0.140 0.725
Import of Goods (Crores)0.511 0.556 0.675 0.338
Export of Services0.328 0.084 0.387 0.147
Import of Services0.491 0.967 0.245 0.385
Statutory Liquidity Ratio0.075 0.192 0.637 0.185
Cash Deposit Ratio0.004 0.006 0.361 0.121
Credit Deposit Ratio0.019 0.053 0.681 0.600
Policy Repo Rate0.176 0.006 0.182 0.782
Reverse Repo Rate0.625 0.129 0.432 0.721
Marginal_Standing_Facility_Rate0.458 0.005 0.013 0.040
Bank_Rate0.458 0.005 0.013 0.040
NSE Turnover0.4600.308 0.555 0.360
NSE Index (Moving Average)0.691 0.476 0.866 0.922
Indicators Used for Analysis with Time Lag IndicatorsQuarterly Time Lag
CommercialVehiclesSalesT-3
TwoWheelers SalesT-0
AirlinePassengers DomesticT-3
AirlineFreight
T-0
RailwaysFreight
T-2
Railways Earnings (Crores)
T-2
EnergyRequirementMUT-3
EnergySuppliedMUT-3
EnergyPeakDemandMUT-0
EnergyPeakSupplyMUT-0
ProductionofCoalinMillionTonnesT-2
ProductionofPetroleumRefineryProductsDollarinThousandTonnesT-2
ProductionofNaturalGasinMillioncubicmetersT-3
ProductionofCrudeOilinThousandTonnesT-3
ProductionofFertilizersinThousandTonnesT-1
Productionof CementinThousandTonnesT-1
ProductionofSteelinThousandTonnesT-0
Exportsof Goods (Crores)
T-0
Importof Goods (Crores)
T-0
Export of Services
USD INR exchange rate
Market capitalization and turnover of NSE
Monthly average price of gold
T-2
T-0
T-0
T-1
Final Lead Indicators Selected Methodology
SincewearestudyingL.I.anditsimpactonGVA,wede-trendtheseries
andobtainthecyclicalcomponent ofthe20selectedlead
indicatorsusingtheHodrick–Prescott(H.P.)filter.Abusinesscycle
describeschangesineconomicgrowthasmeasuredbyGVA.Each
dataseriesoftheallL.I.isconvertedintologarithmform.Wetakethe
StandardDeviation(SD)ofthecyclicalcomponent.Thelogformofthe
dataseriesisdividedbytheSD.Averagethestandardizedseriesacross
allcomponentsforeachquartertoobtaintheQuanttaIndex.Forecast
thetrendofL.I.basedonthepastvalues.Overlaytrendandimpactof
GST/demonetizationonQuanttaIndexandcalculateQuanttaScorey-
o-ygrowthinQuanttascoreispredictedy-o-ygrowthinGVA
Block Granger Test
AtimeseriesofL.I.issaidtoGrangerCauseGVAifthelaggedvaluesof
L.I.providesignificantinformationaboutfuturevaluesofGVA.Obtain
Probability(p)valuesforeachofthesubindicators.Itisusedtoweigh
thestrengthoftheIndicatorsonGVA.Thep-valueisanumberbetween
0and1.Asmallp-value(typically≤0.1)indicatesstrongevidenceofL.I.
impactonGVA.Alargep-value(>0.3)indicatesweakevidence/
impactofL.I.onGVA.Amarginalp-value(0.1-0.3)indicatesmedium
impactofL.I.onGVA.Pvaluesiscalculatedforeachtimeperiod.
LowestpvalueshowshighestcorrelationbetweenL.I.andGVA
ProductsandServicesareusedintheprocessofvalueadditioninthe
Economy.Dependingonthenatureofthesector,differentsectors
theprocessofcreatingtheproductoroutput.
Inordertomeasuretheimpactofasectorontheeconomy,we
lag,measuredasTMinusfromtheperiodunderstudy.Forexample,
asectorthatindicatesthesentimentofthefarmertogrowcrops.The
consumptionoffertilizersshowanarrowtimelagbetweenconsumption
impactontheeconomymakingitaleadindicatoraboutthestateof
economy.Thedomesticpassengertrafficontheotherhandshowsa
lagindicatingthatpeoplepostponetravelwhenuncertaintyincreases
increasetheirtravelwhentheyhaveapositiveoutlookonthehistoric
performanceoftheirbusinessandtheeconomy.Thesesignalsarevital
understandtheunderlyingtrendsintheeconomy.