1

Inflation Dynamics in India: Relative Role of Structural and Monetary Factors

Abstract

This paper studies inflation dynamics in India involving structural and monetary factors over the period

1996:Q1 to 2013Q4. The paper finds that output gap is the most potent factor provoking inflation. Apart

from the output gap, depreciation of exchange rate and broad money growth also stimulates inflation.

Interest rate is identified as an anti-inflationary monetary instrument. In view of a comprehensive policy

for price stability, it is imperative to know the role of sectoral output gap. The paper, therefore, enquires

the role of primary, secondary and the tertiary sector output gap in inflation. Output gap of these three

sectors provokes inflation where the contribution of tertiary sector output gap in inflation is found

maximum followed by the primary sector and the secondary sector output gap. Prominent role of output

gap and comparatively passive role of monetary factors suggests controlling inflation only through the

monetary management would not be effective.

Key Words: Inflation targeting, CPI inflation, interest rate, exchange rate, money growth, output gap,

vector autoregression.

JEL Codes: E31, E52, C32

1. Introduction

Price stability, as defined by a low and stable inflation, is the pre-requisite for financial stability and

economic progress. It is one of the most important objectives of monetary policy of every economy.

Stabilizing inflation with a reasonable economic growth is always a challenging task, particularly for an

emerging economy like India. Inflation in India is a burgeoning issue of concern among the economists,

policy makers and even in the domain of politics. It is an issue that directly affects daily life of common

people and also afflicts a subsistence livelihood of vulnerable poor. Recently, the issue has attracted lots

of attention as the stance of monetary policy of the Reserve Bank of India (RBI) has been shifted towards

inflation targeting trail.

Economic literature has a long controversy between the structuralists and the monetarists regarding the

causes of inflation. Under structuralists view, inflation originates from structural maladjustments,

bottlenecks and rigidities of the economy. This view assessed inflation in a sector specific mode. The

2

output gap, defined as actual less potential output of an economy, is an important factor affecting price

and wage inflation. The output gap and inflation are inversely related. A sustained positive output gap is

indicative of a demand pressure and a signal for an inflationary ambiance. Conversely, a negative output

gap has opposite implication. In fact, if the gap becomes zero, inflation remains stable. Empirical studies

on this issue found evidence that the output gap significantly precede movements in inflation where

output gap may be regarded as an information variable in the formulation of anti-inflationary monetary

policy.

On the other hand, the monetarist viewpoint to the study of inflation has roots in the classical and neo-

classical theories. The quantity theory of money specifies the direct and proportional relationship between

money supply and price level. According to the Fisher’s equation of exchange (Fisher, 1911), any change

in the quantity of money produces proportional change in price level. Keynesians assume a direct but

non-proportional relation between money supply and price level. Monetarists postulate that in the long

run, prices are directly affected by the money growth. Further, monetary policy instruments like interest

rate and exchange rate also affect inflation. The relation between interest rate and inflation is negative.

Higher interest rates discourage borrowing and encourage savings curtailing consumption at the

household level which leads to fall in aggregate demand and stabilize inflation. In a flexible exchange rate

regime, a depreciation of exchange rate causes inflation to rise. Exchange rate variations directly affect

prices of import and export which, in turn, influence the price level through a transmission mechanism

and inflation expectations. Emerging economies having increasing integration with the global economy,

inflationary spiral is often aggravated by the external innovations like international commodity prices.

Such innovations get transmitted through the exchange rate channel. It is a matter of interest to the

economists to enquire how far the exchange rate depreciation has been affected inflation in India since the

inception of the flexible exchange rate regime. In addition to this, from the perspective of recent inflation

targeting monetary stance of the RBI, it is a matter of enquiry that if exchange rate has significant

implication in the recent inflation dynamics in India. Ho and McCauley (2003) view that role of exchange

rate in the evolution of domestic inflation tends to be greater in the case of emerging market economies as

compared to the developed economies.

Apart from these determinants, cheap fiscal policy and a fiscal deficit have inflationary effects.

Moreover, the fiscal theory of the price level (FTPL) presumed that price stability requires not only an

appropriate monetary policy but also an appropriate fiscal policy. Specifically, according to the FTPL the

price level is determined by the ratio of nominal debt to the present value of real primary surpluses. For

3

price stability government finances to be sustainable, fiscal authority should maintain fiscal discipline and

should not allow structural deficit in budget.

Recently some studies have found the evidence that the demographic changes of a country can influence

the inflation. To elucidate, in an economy consumption behavior of the ageing population is different

from that of the young population. Change in population age affects aggregate demand of the economy

which, in turn, affects price level. Bullard et al (2012) argue that the ageing population might prefer lower

inflation than the young due to the redistributive effects of inflation. Anderson et al (2014) in this regard

find that ageing exerts downward pressure on the inflation. Yoon et al (2014) find, based on a panel

regression, that ageing is deflationary. Based on a panel of 22 countries over the period 1955–2010,

Juselius and Takáts (2015) find a stable and significant relation between demography and low-frequency

inflation. In particular, a larger share of dependents is correlated with higher inflation whereas working

age population was correlated with lower inflation. The demographic aspect of inflation in the context of

Indian economy is not researched.

Over the last three decades number of interesting papers (although these are different in respect of time

periods, data frequency, modeling and methodological stance) examined the determinants of inflation in

India (reviewed in the Section 2), no clear-cut consensus has yet emerged. This paper enquires into the

determinants of inflation in India under the monetarists and the structuralists synthesis. Involving recent

time series over the period 1996:Q1 to 2013Q4, the paper attempts to examine the relative role of interest

rate, exchange rate, broad money growth and output gap causing inflation in India. In view of a

comprehensive policy for price stability in India, it is imperative to know the role of sectoral output gap in

inflation which remains scantily researched and documented. The paper also enquires the relative role of

the primary, the secondary and the tertiary sector output gap in inflation. However, demographic and

fiscal variables are not considered in the analysis as the dataset of such variables in India are available

only in annual frequency. Identifying the determinants of inflation may help achieving the targeted

inflation with reasonable economic growth (under the prevailed inflation-growth dynamics) in India.

Further, rigorous empirical analysis is essential for implementing anti-inflationary macroeconomic policy.

The rest of the paper is structured as follows. Section 2 presents related literature. Then, variables, data

and methodology have been presented in Section 3. Section 4 of the paper has focused on empirical

analysis and discussion of findings. Finally, Section 5 concludes the paper with main findings and their

implications.

4

2. Related Literature

Research on inflation in India focused both––the structuralist view and the monetarists view. Applying

structuralist approach, Balakrishnan (1991) models manufactured prices in Indian economy over the

period 1952–1980. The study finds that labor and raw material costs are significant predictors of inflation

in the industrial sector. For the same time span, Balakrishnan (1994) finds that the structuralist model

outperforms the monetarist model. The validity of the structuralist model over the monetarist model of

inflation in India is further supported by Bhattacharya and Lodh (1990). Based on quarterly dataset over

the period 1982Q2 to 1998Q2, Callen and Chang (1998) enquire the indicators provide the most useful

information about future inflationary trends in India. By developing a model of inflation and by

estimating a series of bivariate vector autoregression models, they find that while the broad money target

is de-emphasized developments in the monetary aggregates, it remains an important indicator of future

inflation. The exchange rate, import prices, stock prices and the prices of primary products are also

relevant, particularly for inflation in the manufacturing sector. Estimating structural vector autoregression

model involving quarterly time series over the period 1970Q1 to 1990Q4, Dutta Roy and Darbha (2000)

find that structural factors, in addition to monetary factors, play an important role in generating and

sustaining the process of inflation and fluctuations in economic activity. Nachane and Lakshmi (2002)

study P-Star model for India using both annual and quarterly time series for the period 1955–1995. They

report that it is possible to develop a P-Star model using cointegration techniques to gauge inflationary

pressures in India. Estimated model is well calibrated to data, and in out-of-sample forecasts, it

significantly outperforms a seasonal autoregressive integrated moving average (ARMA) benchmark

model. They also report that the velocity gap version of the model is particularly successful. Ashra,

Chattapadhyay and Chaudhuri (2004) find bidirectional causality between money supply and price level

in India. They report that the relationship between money, fiscal deficit and high-powered money is not

systematic. Patnaik (2010) study the determinants of inflation in India over the period 1991Q2 to 2008Q2

and find a long run relation between consumer price index, the index of industrial production, the reserve

money and the import index. The study claim that inflation in India is mainly demand pull inflation but

the supply side factors which come via the imports also influence inflation in the short run. But the

external factors are not the major determinants of inflation. Kishor (2012) investigates the role of

monetary factors causing inflation in India involving quarterly time series over the period 1982 to 2007.

The study finds that the real money gap is a better predictor of future inflation. In the overall

predictability of inflation, a break at the first quarter of 1995 is also found. Recently, Bhaduri and Durai

(2013) test the effect of excess money growth on wholesale price inflation in India involving Threshold

regression technique and annual time series over the period 1953-54 to 2007-08. They view that the

5

relationship is not linear and without a strong credit growth, excess money growth (M3) has lesser

inflationary effects.

Apart from these studies, variants of the Phillips curve in India are intensively researched. Srinivasan,

Mahambare and Ramchandran (2006) estimate an augmented Phillips curve over the period April 1995 to

March 2005 but do not find evidence. In their model of headline inflation, coefficients on output gap are

positive but insignificant. Such coefficient is found negative when manufacturing headline inflation is

used as the dependent variable. They report that supply shocks only have a transitory effect on both

headline and core inflation, but are not crucial in determining inflation. Srinivasan, Jain and Ramchandran

(2009) argue that the initial rise and subsequent fall in inflation in India during 1960s to early 1990s are

due to the lack of institutional commitment towards price stability. Low inflation in the later period is the

result of positive supply-side developments. Paul (2009) views that due to supply shocks like droughts

and oil crises, and the liberalization-policy shock of the early 1990s the evidence of the Phillips curve in

India is not found. However reconstructing inflation data, an evidence of conventional Phillips curve is

found. Dua and Gour (2010) examine the determinants of inflation for eight Asian countries namely

Japan, Hong Kong, Korea, Singapore, Philippines, Thailand, China Mainland and India in an open

economy forward looking as well as backward-looking Phillips curve framework. They find that the

output gap, import inflation and exchange rate are important determinants of inflation in these economies.

Moreover, both forward looking and backward looking inflation expectations play an important role in

explaining inflation. Mazumder (2011) argues that the Phillips curve exists for India, and the Lucas

critique does not apply empirically to Indian inflation–output data. Singh, Kanakraj and Sridevi (2011)

revisit the existence of the Phillips curve in India. They find that the relationship between inflation and

the output gap, as predicated by the Phillips curve, is relevant for India during first quarter of 2004 and

the first quarter of 2009. Further, the Phillips curve exists only after controlling for supply shocks.

Estimating an open economy version of New Keynesian Phillips curve (NKPC), Sahu (2013) examine the

short run inflation dynamics in India over the period 1996-97 to 2009-10. He finds that the hybrid NKPC

provides a robust explanation of the dynamics of both wholesale price inflation and manufacturing sector

inflation. Moreover, the agricultural and the industrial output gaps, fuel inflation, exchange rate and

foreign inflation are significant determinants of manufacturing sector inflation in India.

6

3. Variables, Data and Methodology

The analysis employs quarterly data covering the period 1996:Q1 to 2013:Q4, taken from the Reserve

Bank of India, hand book of statistics of the Indian economy1. Consumer price inflation has been used as

the measure of inflation. Consumer price index (CPI) data in India till December 2010 is calculated

separately for three groups of people: industrial workers (CPI-IW), urban non-manual and

agricultural/rural people where a composite series is not calculated. For this period CPI-IW data is used.

However, since January 2011 a new CPI data (combining rural and urban people) is available. As the

determinants of inflation, the study involves call money rate as the measure of interest rate, the growth of

M3 measure of money supply (seasonally adjusted, by Census X12 method), rate of change of rupee/US

dollar exchange rate, which is intended to capture the degree of pass-through from the exchange rate to

domestic prices; output gap of the economy as a whole and that of the primary sector, the secondary

sector and the tertiary sector.

The output gap is defined as the difference between actual and potential output of an economy. The

potential output and the output gap are not observed directly and need to be constructed using information

from other economic aggregates which are observed. Among the various methods for the estimating

output gap2, the study uses Hodrick-Prescott (HP) filter (Hodrick and Prescott, 1997). Figure 1 to Figure 4

show the output gap of the Indian economy, the primary sector, the secondary sector and the tertiary

sector respectively. A brief description of the variables chosen for empirical analysis has been presented

in Table 1.

Figure 1: Output GAP of the Economy as a Whole ( )

1The quarterly series of inflation, money supply, interest rate and exchange rate are generated from the

monthly series of variables concerned (as these series are not readily available in the RBI databank). 2 Each of these methods has substantial uncertainty and requires considerable judgment. Brouwer (1998)

in this context viewed that the estimation of the output gap is imprecise, can vary considerably across

estimation methods. According to Coe and McDermott (1997) estimating trend or potential output is more

an art than a science.

-.100

-.075

-.050

-.025

.000

.025

.050

.075

.100

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13

7

Figure 2: Output GAP of the Primary Sector ( )

Figure 3: Output GAP of the Secondary Sector ( )

Figure 4: Output GAP of the Tertiary Sector ( )

Table 1: Variables and Representation

Variables Specification Representation

Inflation Consumer price inflation is calculated from the seasonally

adjusted consumer price index with the base period 2005.

Interest rate Call money rate. Exchange rate Rate of change of rupee/US dollar nominal exchange rate.

Money Growth Seasonally adjusted real M3 money growth.

-.100

-.075

-.050

-.025

.000

.025

.050

.075

.100

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13

-.100

-.075

-.050

-.025

.000

.025

.050

.075

.100

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13

-.100

-.075

-.050

-.025

.000

.025

.050

.075

.100

96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13

8

Output Gap

Calculated from the seasonally adjusted real output (GDP)

by the HP filter. Actually, it is the deviation of trend GDP

from the seasonally adjusted GDP.

Primary Sector

Output Gap

Calculated from the seasonally adjusted real primary sector

output by the HP filter.

Secondary Sector

Output Gap

Calculated from the seasonally adjusted real secondary

sector output by the HP filter.

Tertiary Sector

Output Gap

Calculated from the seasonally adjusted real tertiary sector

output by the HP filter.

Author’s representation

For empirical analysis the paper involves a battery of time series econometric techniques. Augmented

Dickey-Fuller (1979, 1981; ADF, henceforth) and the Phillips-Perron (1988; PP, henceforth) unit-root

tests have been used for confirming stationarity and the order of integrability of variables. Vector

autoregression (VAR) model has been used identifying causal relation and the dynamics of inflation

maintained with interest rate, rate of change of exchange rate, money growth and the output gap. Impulse

response functions have been used for accessing the dynamic impacts of endogenous innovations on

inflation. If the estimated dynamics is maintained at the near future, then how the inflation profile get

constituted through these endogenous random innovations are ascertained through a variance

decomposition analysis. This analysis has a scope for identifying the relative strengths of innovations

affecting inflation in the out-of-sample forecast horizon. The paper refrains from rehashing the technical

details of these techniques because of their popularity but specifies only the estimable form of the VAR

model involving selected variables.

A VAR model involving inflation, interest rate, the rate of change of exchange rate, money growth and

output gap has been estimated. Among the five equations of the VAR model, the relevant equation for

this study, the equation of inflation is as under.

(1)

where, and , and (i =1, 2,.., k) are lagged series of CPI inflation, interest rate,

rate of change of exchange rate, broad money growth and output gap respectively. The variable ‘n’ is the

optimum lag length while represents innovation that may be contemporaneously correlated but are

uncorrelated with their own lagged terms and with all of the right-hand-side variables.

9

4. Empirical Analysis and Discussion

Variables represented in Table 1 are expected to be stationay. However, stationarity and the order of

integrability of the variables have been checked by ADF and PP unit-root tests. Results of these tests, as

reported in Table 2, clearly indicate that unit-root of the variables examined herein is rejected even at 1

per cent level. Unit root tests conducted herein confirm that variables selected are stationary or order of

integrability of each of the variables is I(0).

Table 2: Results of Unit-Root Tests

Variables

H0: the series has unit-root

ADF Statistic Prob. PP statistic Prob.

-5.990

-4.713

-6.357

-6.288

-9.014

-4.475

-3.809

-5.295

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

-5.966

-4.794

-6.355

-6.261

-10.228

-4.627

-3.322

-5.026

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

Notes: (i) in both ADF and PP tests the assumption exogenous constant is taken

(ii) in ADF test, lag length is selected based on Schwarz Information Criterion

and in PP test bandwidth is selected using Bartlett kernel.

Source: Author’s own research

As the set of variables involved in the study are testified as stationary, these variables have been used in

the estimation of the VAR model. As per the objective of the study the relevant VAR equation, the

estimated equation of inflation is presented in Table 3. In this estimation lag length 4 is taken based on

Akaike information criterion (AIC). Here, statistically significant positive estimate of inflation at the lag

one reflects inflation persistence say, through inflationary expectations or actual price rigidities while the

negative sign of the estimate (significant at 10 per cent level) at the lag three indicates that inflation series

possibly a mean-reverting or stationary series. Significant lagged estimates of interest rate, rate of change

in exchange rate, broad money growth and the output gap indicate that each of these variables Granger

cause inflation. Specifically, significant negative estimate of interest rate at the lag two implies that two

quarter lagged interest rate dampens inflation. The negative impact of interest rate on inflation is in

conformity with the macroeconomic theory that lowers the lending rate of interest, lower is the cost of

capital. This lower cost of capital causes investment spending and aggregate demand to a rise which may

lead to higher economic growth as well as inflation. Significant positive estimates of the rate of change in

exchange rate at the lag 2 and 4 signify a pass-through of depreciation of the rupee in terms of the US

dollar, which provokes inflation. To elucidate this phenomenon, a steady spell of depreciation of rupee

10

over the period of the study results increase in the prices of foreign goods, petroleum, machineries and

other essential raw materials imported for the Indian economy. Higher price of these goods, more

importantly higher prices of petroleum and essential inputs, through the cascade effect, increase cost of

production which promotes inflation. Significant negative estimate of the broad money growth at the lag 3

and positive estimate at the lag 4 indicate broad money growth causes variations in inflation. Apart from

the monetary determinants, significant positive estimate of the output gap at the lag 1 and lag 2 indicate

that the output gap is another important determinant provoking inflation in India.

Table 3: Inflation Dynamics through the VAR Estimation

Dependent Variable:

Parameters Estimates ‘t’-statistic Probability

0.0019

0.7097

0.2042

-0.5060

0.3951

0.0446

-0.2238

0.1124

-0.0586

0.0171

0.1703

-0.0138

0.1135

0.2521

0.0910

-0.3982

0.4113

0.3306

0.4006

-0.0446

0.1712

0.1937

3.0857

0.7364

-1.7806

1.5389

0.5006

-2.4111

1.1538

-0.6414

0.3521

3.4952

-0.2383

2.0566

1.3827

0.4574

-1.9730

2.1741

2.9778

3.4527

-0.3325

1.3393

0.84

0.00

0.46

0.08

0.13

0.61

0.01

0.25

0.52

0.72

0.00

0.81

0.04

0.17

0.64

0.05

0.03

0.00

0.00

0.74

0.18

Diagnostic

Statistic

R2 = 0.555, Adjusted R

2 = 0.366,

DW = 1.987, F-Statistic = 2.935

Source: Author’s research

Source: Author’s own research

Having identified the causal role of the selected determinants on inflation, the paper attempts to assess the

dynamic impact of endogenous innovations on inflation. In this endeavor, impulse response functions

(residual one SD) are derived from the estimated VAR model and presented through Figures 5(a)–5(e).

The response of inflation to a positive innovation transmitted through inflation channel (i.e., the response

to its own innovation), as presented in Figure 5(a), reveals that inflation jumps to high level and remains

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up to a period of four quarter. An inflation innovation, therefore, provokes present inflation which may be

due to the inflationary expectations. A positive innovation transmitted through the interest rate channel

strives down and keeps inflation below the base line. So, a positive interest rate innovation has executed

as an anti-inflationary monetary policy instrument. But inflation shoots to a high level to a positive

impulse of exchange rate innovation and confirms that the rupee depreciation amplifies innovation. A

positive money growth innovation also shoots inflation up from the base level, ushers fluctuation of

inflation and gradually becomes frail. Inflation, on the other hand, jumps to a significantly high level due

to the output gap innovation. This innovation also leads a periodic fluctuation of inflation around its base

level before its gradual abolition.

Figure 5 (a): Response of inflation to inflation innovation

Figure 5(b): Response of inflation to interest rate innovation

Figure 5(c): Response of inflation to exchange rate innovation

-.012

-.008

-.004

.000

.004

.008

.012

2 4 6 8 10 12 14 16 18 20

-.010

-.008

-.006

-.004

-.002

.000

.002

.004

.006

2 4 6 8 10 12 14 16 18 20

-.006

-.004

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20

12

Figure 5(d): Response of inflation to money growth innovation

Figure 5(e): Response of inflation to supply shock innovation

Source: Derived from the VAR model presented in Table 3

Having analyzed the dynamic impact of endogenous innovations on inflation, it may be comprehensive if

the relative strengths of these innovations can be identified. For this purpose, a variance decomposition

analysis, a percentile decomposition of forecast error variances of inflation by these innovations in the

out-of-sample periods up to twelve forecast quarters is presented in Table 5. The output gap innovation

accounts about 29 per cent variations of inflation at 12 quarter horizon. At the same horizon, the interest

rate, money growth and exchange rate innovation accounts 8.79 per cent, 8.74 per cent and 7.6 per cent

variance of inflation respectively. It is examined that the relative role of these innovations remains same

once the Cholesky ordering of innovations is changed. In short, in the constitution of an inflation profile

in the near future, the output gap would play the most significant role where as the role of monetary

factors would be weak.

-.0100

-.0075

-.0050

-.0025

.0000

.0025

.0050

.0075

.0100

2 4 6 8 10 12 14 16 18 20

-.004

-.002

.000

.002

.004

.006

.008

.010

.012

2 4 6 8 10 12 14 16 18 20

13

Table 4: Variance Decomposition of Inflation (

Period

ahead

innovation

innovation

innovation

innovation

innovation

1

2

3

4

5

6

7

8

12

100.0

76.25

55.11

54.23

55.14

53.56

52.16

49.89

45.77

0.00

4.18

3.51

3.43

3.31

4.41

5.77

5.52

8.79

0.00

1.78

4.78

4.69

5.82

5.70

6.16

6.74

7.60

0.00

1.04

1.79

3.55

3.92

5.04

4.91

6.29

8.74

0.00

16.75

34.80

34.09

31.79

31.28

31.00

31.55

29.09

Source: Derived from the VAR model presented in Table 3

Sectoral Inflation Dynamics:

Above analysis testifies that apart from the monetary factors, the output gap is a potent factor provoking

inflation. In view of a comprehensive policy for price stability, it is imperative to know the role of

sectoral output gap abreast of overall output gap in inflation. This sub-section presents the relative role of

the primary, the secondary and the tertiary sector output gap in inflation. In this endeavor following

equation is considered:

(2)

Equation (2) is a form of the Phillips curve, specifically an inflation-expectations-augmented variant of

the Phillips curve with adaptive expectations3. Here,

is inflation expectation at period ‘t’, is the

output gap at the period ‘t-i’ (i =1,2,…n), ‘n’ is the optimum lag length and is the stochastic error term.

Under an adaptive expectation, can be replaced by . Under this line of reasoning, current inflation

is expressed as a function of lagged inflation, which captures the inflation inertia element. If this be the

case, Equation (2) becomes:

(3)

Further, if it is assumed that the inflation expectations are formed based on the inflation occurred in the

past periods ( instead of inflation occurred in the recent past period ( , Equation (3) modifies to:

(4)

3 which involves price expectations and output gap instead of unemployment gap. The use of output gap

instead of unemployment gap is appropriate if goods market responds quickly than the labor market

following a shock.

14

From the econometric point of view Equation (4) is a representation of VAR model of inflation (and

output gap). Estimating this equation involving the sectoral output gap it would be possible to identify the

role of the output gap in inflation. In continuation, impulse response functions and a variance

decomposition analysis would provide a clear portrait of the output innovation over time as well as the

contribution of the sectoral output gap promoting inflation in the period ahead.

Accordingly, Equation (4) is estimated involving aggregate output gap4 as well as the output gap of the

primary, the secondary and the tertiary sector. Estimated equations as presented in Table 5 are denoted by

Model 1, Model 2, Model 3 and Model 4 respectively. In each of the four cases, one period lagged

inflation causes a raise in current inflation implying that an inflationary expectation leads raise inflation.

Further, inflation is Granger caused by the output gap of the economy as whole and that of the other three

sectors. Specifically, one and four period lagged output gap of primary sector push inflation up where as

such output gap at the lag two pulls inflation downwards. Like the primary sector gap, the tertiary sector

gap also provokes inflation. The secondary sector output gap pushes inflation up with a lag period of one

quarter. Figures 6(a)–6(d), visualizing the impulse response functions of inflation to one SD innovations

of each of the four gap innovation, reveal that inflation jumps to a high level due to these innovations.

This stance of inflation subsequently dies off implying that the output gap innovations are short-lived.

Among these innovations, inflation responds the maximum due to the gap innovation of the tertiary sector

followed by that of the primary and the secondary sector. The movement of inflation to the aggregate

output gap innovation obviously shows the overall picture of three gap innovations.

4 Which would check the robustness of the finding that output gap promoting inflation in India.

15

Table 5: Output GAP and Inflation (Estimated VAR Equation 4)

Model 1

Dependant Variable:

Model 2

Dependant Variable:

Model 3

Dependant Variable:

Model 4

Dependant Variable:

Parameters Estimates

‘t’-

statistic Parameters Estimates

‘t’-

statistic Parameters Estimates

‘t’-

statistic Parameters Estimates

‘t’-

statistic

0.007

0.465

0.142

-0.054

-0.022

0.408

-0.080

-0.267

0.213

2.382b

3.248a

0.917

-0.342

-0.196

3.993a

-0.531

-1.721c

1.722c

0.009

0.452

-0.027

-0.015

-0.005

0.217

-0.196

-0.060

0.159

2.963a

3.454a

-0.189

-0.104

-0.044

2.968a

-2.245b

-0.659

2.038b

0.011

0.330

–

–

–

0.112

–

–

–

4.719a

2.995a

–

–

–

2.352b

–

–

–

0.007

0.395

0.164

-0.025

0.033

0.434

0.049

-0.211

0.227

2.315b

2.890a

1.201

-0.172

0.294

5.409a

0.452

-1.964c

2.279b

Diagnostic

Statistic

= 0.391, =

0.307, F = 4.66,

DW = 2.02

Diagnostic

Statistic

= 0.272, =

0.171, F = 2.712,

DW = 2.11

Diagnostic

Statistic

= 0.182, =

0.157, F = 7.46,

DW = 2.05

Diagnostic

Statistic

= 0.500, =

0.431, F = 7.254,

DW = 2.02

Notes:

Superscripts a, b and c indicate the level of significance of the ‘t’-statistic at 1 percent, 5 percent and 10 percent level respectively.

Lag length is determined based on AIC.

Source: Author’s own research

16

Figure 6(a): Response of Inflation to Aggregate Output Gap ( ) Innovation

Figure 6(b): Response of inflation to primary sector output gap ( ) innovation

Figure 6(c): Response of inflation to secondary sector output gap ( ) innovation

Figure 6(c): Response of inflation to tertiary sector output gap ( ) innovation

Source: Derived from the VAR models presented in Table 5

-.004

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20

-.004

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20

-.004

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20

-.004

-.002

.000

.002

.004

.006

.008

.010

2 4 6 8 10 12 14 16 18 20

17

Finally, the percentile decomposition of variance of inflation due to its own innovation and the concerned

gap innovations up to 12 forecast horizon have been presented in Table 6. It is found that overall output

gap innovation would account 30 per cent variance of inflation at the 3 forecast period horizon and 33 per

cent of such variance at the 12 forecast period horizon. Among the gap innovations of the sub sectors, the

secondary sector gap innovation would account the lowest percentage variance (5.93 and 10.34 per cent at

the 3 and 12 period horizon respectively) and the tertiary sector gap innovation would account the highest

percentage variance (45.4 and 47.3 per cent at the 3 and 12 period horizon respectively) of inflation. The

variance decomposition analysis, therefore, confirms that in the near future the tertiary sector output gap

may be an important cause for higher inflation in India.

Table 6: Variance Decomposition of Inflation (

Period

ahead

Model 1

Model 2

Model 3

Model 4

1

2

3

4

5

6

7

8

12

100.0

82.00

69.73

69.67

69.70

69.20

68.88

67.31

66.83

0.000

18.00

30.27

30.33

30.30

30.80

31.12

32.69

33.17

100.0

89.68

89.89

88.92

87.96

87.98

87.42

86.83

86.74

0.000

10.32

10.11

11.08

12.04

12.02

12.58

13.17

13.26

100.00

97.24

94.07

91.95

90.79

90.20

89.91

89.78

89.67

0.000

2.76

5.93

8.05

9.21

9.80

10.08

10.22

10.34

100.00

69.33

54.53

54.26

53.80

53.70

53.50

52.76

52.68

0.000

30.67

45.47

45.73

46.20

46.30

46.49

47.24

47.32

Source: Derived from the VAR models presented in Table 5

5. Conclusions

This paper studies the inflation dynamics in India involving interest rate, exchange rate, broad money

growth and output gap over the period 1996:Q1 to 2013Q4. Estimated vector autoregression model

followed by impulse response functions and a variance decomposition analysis have found that output gap

is the most potent factor stimulation inflation. Among the monetary factors, interest rate is identified as an

anti-inflationary monetary instrument. Depreciation of Indian rupee and the broad money growth also

stimulates inflation. Variance decomposition analysis confirms that output gap innovation would account

around 29 per cent of forecast error variance of inflation in the 12 forecast horizon. Whereas the

monetary instruments like interest rate, exchange rate and money growth would account 8.7, 7.6 and 8.7

per cent of such variance respectively. This study also has analyzed the relative role of the primary, the

secondary and the tertiary sector output gap in inflation. Output gap of these three sectors also Granger

18

causes and provokes inflation where the contribution of the tertiary sector output gap is found maximum

followed by the primary sector and the secondary sector output gap.

Prominent role of the output gap and comparatively passive role of the monetary factors in inflation

suggests controlling inflation only through the monetary management would not be effective. A long term

policy for removing bottlenecks and rigidities in the economic system abreast of the inflation targeting

monetary policy is also necessary. Moreover, some additional factors that compress inflation (food

inflation in particular) in India are inefficient pricing and marketing of agricultural commodities, black

marketing, hoarding. These issues are related to the governance of union and state governments.

Strengthening of public distribution system is essential to rescue the vulnerable poor from inflationary

hardship. Coordination between the inflation targeting monetary policy of the RBI and a comprehensive

economic policy of union and state governments is the utmost important.

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