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Impact of oil price shocks on macro-economy: evidence from an oil

importing developing country

Sajal Ghosh

assistant professor economics, mdi, Gurgaon, india

Address: Room No C-10, Scholar Building, MDI, Mehrauli Road, Sukhrali, Gurgaon

122001, India. Email: sghosh@mdi.ac.in, sajalg@yahoo.com; Phone: +91-124-4560309,

Fax: +91-124-2340147

Kakali Kanjilal*

associate professor qualitative techniques & operations management, imi, new delhi,, india

Address: International Management Institute, B-10, Qutab Institutional Area,Tara

Crescent, New Delhi 110016, India. Email: kakali@imi.edu; Phone: +91-11-

47194100, Fax: +91-11-26867539

*Corresponding author

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Abstract

The study investigates the dynamic impact of linear and various non-linear specifications

of oil price shocks on some macroeconomic variables for an oil importing developing

country India during the period March 1991 to January 2009. The paper deploys

extended VAR model of possibly integrated processes proposed by Toda and Yamamoto,

which has its advantage of application irrespective of the variables being stationary or

cointegrated. The article examines Granger causality between oil price shocks and

macroeconomic variables. The study also examines generalized error variance

decomposition and impulse response paths of macroeconomic variables due to oil price

shocks. The study confirms that movement in oil price is exogenous with respect to

Indias macroeconomic movements and the impact of oil price shocks are asymmetric in

nature with negative price shocks having more pronounced effect on macroeconomic

variables than positive shocks.

Keywords: oil price shocks; India; asymmetric impact; vector autoregression; economy

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1. Introduction

World crude oil price has entered into an era of higher price volatility due to

geopolitical uncertainties, supply constraints, high refinery utilization and high demand

growth (Kesicki, 2009). From a theoretical perspective, impact of oil price shocks can be

transmitted to economic activities through following six channels (Brown and Ycel,

2002)

1. Supply-side shock effect; focusing on the negative impact on output due to the

increase of marginal production costs caused by positive oil price shock. This

would also has a negative impact on employment

2. Wealth transfer effect; indicating the transfer of wealth from oil importing

countries to oil exporting countries and hence deteriorating terms of tread for

oil importing countries.

3. Real balance effect; where an increase in increase in oil prices would lead to

increase in money demand. When monetary authorities fail to increase money

supply to meet growing money demand, there would be a rise in interest rate

and a retard in economic growth.

4. Inflation effect; where a rise in oil price generates inflation. When the

observed inflation is caused by oil price-increased cost shocks, a

contractionary monetary policy can deteriorate the long term output by

increased interest rate and decreased investment.

5. Sector adjustment effect works via effects of oil price shocks on the labour

market by changing relative production costs in some industries, and

6. Unexpected effects; focusing on the uncertainty over oil price and its impact.

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Dynamic relationship of oil price and macro economy has been the area of

empirical research for many authors for the last three decades especially post the first oil

embargo in 1973. Pioneering work in this regards carried out by Darby (1982) and

Hamilton (1983) based on US economy. Hamiltons linear impact model (1983) has later

been extended to non-linear models by Mork (1989), Hamilton (1996) and Lee et al

(1995), which propose to consider the asymmetric responses by separating the oil price

variable into upward and downward movements. Various other studies examining oil

price macroeconomic relationship for developed countries include Gisser and Goodwin

(1986), Mork and Olsen (1994), Guo and Kliesen (2005), Lardic and Mignon (2006),

Chen and Chen (2007), Cologni and Manera (2008), Jimenez-Rodriguez (2008) among

others.

Despite main focus on oil price macroeconomic research directed towards oil

importing developed countries, some recent studies have examined the same for

developing countries like Philippine (Raguindin and Reyes, 2005), Venezuela (El-

Anashasy, 2005), Nigeria (Iwayemi and Fowowe, 2011), Iran (Farzanehan and

Markwardt, 2009), Thailand (Rafiq et al., 2009), Tunisia (Jbir and Zouari-Ghorbel, 2010)

and China (Cong et al., 2008; Tang et, al., 2010; Du et al., 2010)

Rafiq et al (2009) while summarizing previous studies revealed that oil price

shocks have significant asymmetric impact on macroeconomic fundamentals; the

negative shocks having much larger impact than the positive ones.

The novelty of this paper is as follows:

a) It explores, for the first time, the effect linear and non-linear oil price shocks on

macroeconomic variables for India, a major oil importing development country, for

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the period March 1991 to January 2009. The period considered for the study comes

under the highly subsidized, regulated regime of petroleum product prices in India.

b) Under such regulated environment, it is interesting to explore if the macro

fundamentals of Indian economy are truly insulated from international oil price

shocks in spite of the constant effort by the Indian government to isolate the economy

from international oil price volatility by pumping huge subsidy to the sector for

decades.

c) The study employs extended Vector Auto-regression (VAR) model proposed by Toda

and Yamamoto (1995) hereafter TY. The two main advantages of TY method are;

first it can be used irrespective of order of integration of underlying variables and

second irrespective of whether or not the variables are cointegrated.

2. Indian oil sector A brief overview

India imports more than 70 per cent of its crude requirements due to limited

supply and stagnation in domestic production. Integrated Energy Policy (IEP) document

published by Planning Commission in India, projects that the share of oil in Indias

primary commercial energy mixes is expected to go down by 29.43 per cent in 2030-31

which was 36.39 per cent in 2003-04. In 1997, the Government announced the New

Exploration Licensing Policy (NELP) to encourage investment in the exploration &

production of domestic oil & gas. InNELP regime, several gas discoveries were

successful, but the number of oil field discoveries remained miniscule. Energy security in

India is in danger due to burgeoning oil import bill and increasing geopolitical

disturbances.

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In India, retail price of petroleum products are mostly under government control.

Public sector oil marketing companies (OMCs) are forced to sell their products at prices

below the costs of procurement at the refinery gate and are not allowed to modify the

price of petroleum products based on the fluctuation of international crude market

resulting under recoveries. In 2007-08, the under-recoveries suffered by OMCs in selling

kerosene, liquefied petroleum gas (LPG), petrol and diesel was Rs 771.23 billions. The

price of petroleum products in India is further distorted by heavy taxes imposed by

government on consumers. Government recently decontrolled petrol price at retail level

and is planning to do the same for diesel.

3. Data Description & Methodology

The data considered for the study is March 1991 to January 2009. Monthly

average price of UK Brent crude, West Texas Intermediate (WTI) and Dubai Crude, in

dollar terms, have been collected from the website of Energy Information Administration

(EIA) of US. This has been taken as a proxy of international oil price and is further

deflated by US consumer price index (CPI) to get real oil price. Other macroeconomic

variables, Index of Industrial Production, Real Exchange Rate, Real Exports, Call money

rate, real foreign exchange reserve and WPI have been collected from the website of

Reserve Bank of India. We have taken Index of Industrial Production (IIP) and average

call money rates of major commercial banks in India as a proxy to income and interest

rate or monetary policy indicator. oil, ex, iip, int, exp and forex represent real

international oil price, real exchange rate, index of industrial production, interest rate, real

export and real foreign exchange reserves after logarithmic transformation while inf

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represents WPI inflation and is not undergone log transformation. Choice of the variables

is based on the availability of the data at the time of analysis.

3.1 TY version of Granger causality

The conventional Granger causality tests in an unrestricted VAR framework is

conditional on the assumption that the underlying variables are stationary, or integrated

of order zero in nature. If the time series are non-stationary, the stability condition of the

VAR is supposed to be violated. This implies that the 2 (Wald) test statistics for Granger

causality that are used to test the joint significance of each of the other lagged

endogenous variables in VAR equations becomes invalid. In the case of non-stationary

time series, one must investigate cointegration and if that exists, one must proceed with

vector error correction model instead of unrestricted VAR. If the series are not integrated

of orderI(1) or are integrated of different orders no test for long run relationship is

employed. On the other hand employment of unit root and cointegration tests may suffer

from low power against the alternative therefore they can be misplaced and may suffer

from pre-testing bias (Toda and Yamamoto, 1995; Pesaran et al, 2001). To obviate some

of these problems Toda &Yamamoto (1995) and Dolado and Lutkepohl (1996) employ a

modified Wald test for restriction on the parameters of the VAR ( k) with kbeing the lag

length of the VAR system. In their approach the correct order of the system (k) is

augmented by the maximal order of integration (dmax) then the VAR(k + dmax) is estimated

with the coefficients of the last lagged dmaxvector being ignored. Toda and Yamamoto

(1995) confirm that the Wald statistic converges in distribution to a chi-square random

variable with degrees of freedom equal to the number of the excluded lagged variables

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regardless of whether the process is stationary, possibly around a linear trend or whether

it is cointegrated.

The TY procedure avoids the bias associated with unit roots and cointegration

tests as it does not require pre-testing of cointegrating properties of the system (Zapata &

Rambaldi, 1997 and Clark & Mizra, 2006). The method proposes an augmented level

VAR modeling and hence causality testing with a possibly integrated and cointegrated

system (of arbitrary orders) unlike the general VAR modeling where the long-run

information of the system is often sacrificed in the mandatory process of first

differencing and pre-whitening (Clark & Mirza, 2006; Rambaldi and Doran,2006). The

test (MWALD) statistic is valid as long as the order of integration of the process does

not exceed the true lag length of the model (Toda & Yamamoto, 1995).

However, TY approach has some weaknesses as well. The approach is inefficient

and suffers some loss of power since the VAR model is intentionally over-fitted (Toda &

Yamamoto, 1995: 247). Kuzozumi & Yamamoto (2000: 212) also warn that for small

sample size, the asymptotic distribution may be a poor approximation to the distribution

of the test statistic.

A VAR of order p can be represented by

where yt is a (n 1) vector of endogenous variables, t is the linear time trend, a0 -

and a1 are (n 1) vectors, wt is a (q 1) vector of exogenous variables and ut is a (n 1)

vector of unobserved disturbances where ut N (0, ), t = 1 2..T.

In our case, TY version of VAR(k+ dmax) can be written as

8

)1.......(1

10 ttit

p

i

ituwytaay ++++=

=

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.....

..................................

.....

.....

......

exp

intinf

.....

.............................

.....

.....

.....

exp

intinf

,77,73,72,71

,37,33,32,31

,27,23,22,21

,17,13,12,11

1

1

1

1

1

1

1,771,731,721,71

1,371,331,321,31

1,271,231,221,21

1,171,131,121,11

6

5

4

3

2

1

++

+

=

kkkk

kkkk

kkkk

kkkk

t

t

t

t

t

t

t

t

t

t

t

t

AAAA

AAAA

AAAA

AAAA

iip

exdoil

AAAA

AAAA

AAAA

AAAA

iip

exdoil

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where dis the first-difference operator and the order of p represents (k+ dmax). Directions

of Granger causality can be detected by applying standard Wald tests to the first kVAR

coefficient matrix. For example,

H01: A12,1 = A12,2 = . = A12,k= 0, implies that ex does not Granger cause doil

H02: A21,1 = A21,2 = . = A21,k= 0, implies that doildoes not Granger cause ex

H03: A13,1 = A13,2 = . = A13,k= 0, implies that iip does not Granger cause doil

H04: A31,1 = A31,2 = . = A31,k = 0, implies that doildoes not Granger cause iip and so

on.

4. Empirical Results

At the beginning one must identify the maximum order of integration (dmax) of the

underlying variables as well as optimal lag length (k) of the VAR system. Table 1

presents the results of unit root tests based on augmented Dickey-Fuller (ADF), Phillips-

Perron (PP) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) statistics on the levels and

the first differences of the variables. In ADF and PP tests, the null hypothesis is the

series has a unit root against the alternative of stationarity, while for KPSS the null

hypothesis is the series is stationary. Thus, KPSS is used to complement ADF and PP

tests in order to have robust results. The results of unit root tests reveal that all series are

I(1) in nature except intand inf. Since we have both I(0) and I(1) series, it is appropriate

to employ TYDL extended VAR model and as Table 1 suggests, dmax should be equal to

one.

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On the basis of Schwarz Bayesian (SBC) and adjusted log-likelihood ratio (LR)

Test Criteria, the optimal lag order of the VAR is chosen as 2. The absence of residual

serial correlation of the individual equations has also confirmed the correct order of VAR

selection. In the next stage, we augment the VAR by the maximum order of integration of

the series (dmax) and estimate VAR(3) model. The residual series passes the required

diagnostic tests for serial correlation, heteroscedasticity, miss-specification of functional

form and normality. We have also introduced seasonal dummies as exogenous I(0)

variables and all of them are found to be statistically significant though the dummy

variables introduced to capture the effects of South-East Asian Crisis and terrorist attacks

of September 11, 2001 are found to be statistically insignificant in the VAR framework.

a) Linear Impact

Here, it has been assumed that the impact of international price shocks (doil) on

Indian macro-economy is linear in nature. Table 2 presents the results of the TY version

of the Granger causality tests. It shows that the null hypothesis of non-causality from oil

price shocks to inflation and foreign exchange reserves have been rejected. However, no

causality is found from oil price shocks to IIP, exchange rates, interest rate and exports.

This suggests that though the movements in international oil price dont affect most of

the macro fundamentals; IIP, real exchange rate, real exports and interest rates, it has an

impact on inflation and foreign exchange reserve in the economy. This finding is intuitive

and mostly consistent with the fact that the oil price market in India is under government

regulation and the economic growth has not been impacted by the oil price volatility. The

eye-opener finding is that the regulation in the oil sector could not isolate the economy

from having impact on inflation and foreign exchange reserves. But this impact of oil

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price shocks on inflation and foreign exchange reserves cannot be directly explained by

any of the six oil-price shock impact channels by Brown and Ycel, (2002) as the sector

is regulated. The direction of causality could possibly be better explained by increasing

trend of government borrowing in order to meet the rising oil import bill and maintain the

huge subsidy to the oil sector. Resultant is the increase in fiscal deficit, reduction in

foreign exchange reserves in India and increase in money demand. The non-causality in

the opposite direction suggests that global oil price is not impacted by any of the

domestic macro variables.

Detecting Granger causality is an in-sample phenomenon, which is useful in

discriminating Granger exogeneity or endogeneity of the dependent variable within

sample period, but is unable to deduce the degree of exogeneity of the variables out of

sample period. To address this, in the next stage, we employed generalized impulse

response function (GIRF) and forecast error variance (GFEV) decomposition analysis.

GFEV is the percentage of the variance of the error made in forecasting a variable due to

a specific shock at a specific time horizon. Accordingly, the variance decompositions

provide natural measures of the relative importance among these variables that affect the

variance of another variable. The GIRF analysis, on the other hand, can trace out the

dynamic responses of one variable to innovations or shocks in another variable.

It is important to note that unlike the standard orthogonalized approach, the

generalized approach of Pesaran and Shin (1998) is not sensitive to the ordering of

variables in the VAR system. Unlike orthogonal approach, the values for generalized

variance decomposition at each horizon do not necessarily sum to one.

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The results of GFEV analysis on macroeconomic variables due to oil price shocks

are shown in Table 3 over a horizon of 1, 6 and 12 months. Majority of forecast error

variance due to the shock in international oil price at the end of 12 th month is explained

by its own shock (~90%) followed by foreign exchange reserve (4.6%), inflation (2%)

and exports (~1.8%). Results are in line with the expectation and consistent with

causality tests suggesting that international oil price shocks fail to impact most of the

macro fundamentals though it has the highest impact on foreign exchange reserves. 4.6%

variance in foreign exchange reserve can be attributed to one of the source of internal

government funds to meet the oil import bill.

Figure 1 reveals generalized impulse response (IR) paths of endogenous variables

in the VAR system due to one unit standard error (SE) shock in the equation of doil. The

largest impact has been observed on inflation. Oil price shock has a positive impact on

inflation and the impact reaches its maximum value in the first month after the shock.

There is a dip in the second month followed by another peak in third month. After that,

the influence starts declining and becomes very small and negative after seven months.

b) Asymmetric Impact

It has been argued in the literature that the oil price shocks and macroeconomic

relationship is non-linear and many studies suggested the possibility of asymmetric

impact of oil price shocks on macroeconomic variables. Asymmetric impact implies that

the macroeconomic consequences of increase in oil price are not the mirror image of

decrease in oil price.

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To examine the non-linearity and asymmetric impact of oil price shocks, we

consider a few non-linear transformations of oil prices following the methods pioneered

by Mork (1989), Hamilton (1996) and Lee et al. (1996).

Mork (1989) proposes an asymmetric definition of oil prices, which distinguishes

between positive and negative changes, which have been defined as follows:

Real oil price increase: doilt(+) = max [0, doilt]

Real oil price decrease: doilt(-) = min [0, doilt] ..(3)

Hamilton (1996) proposed the concept of net oil price increase/decrease. Net oil

price increase (NOPI), which is the percentage change of the increase of oil price if the

price of the current month (t) exceeds the twelve previous months maximum. If the price

of month (t) is lower than it had been at some point during the previous twelve months,

the series is defined to be zero for period ( t). So,

NOPIt = max [0, oilt max (oilt-1, oilt-2, oilt-3oilt-12)]

Similarly, net oil price decrease (NOPD) can be defined as,

NOPDt = min [0, oilt min (oilt-1, oilt-2, oilt-3oilt-12)] (4)

Lee et al. (1995) proposed to transform the oil price by the AR(12)-GARCH(1,1)

error process as the frequent and erratic oil price movements could have different impact

on real GNP as opposed to the stable oil price movements.

The proposed AR (12)-GARCH(1,1) error process is as follows:

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)5.....().........

,0min()(

),0max()(

)1,0(~

12

2

11

12

1

t

t

t

t

t

t

ttot

tttt

tit

i

it

hO

hO

hh

Nvhv

oilconstoil

=

=+

++=

=

++=

=

Ot(+) and Ot(-) represent positive and negative oil price volatilities.

The results of the Granger causality tests between transformed oil price shocks

and other macro variables in the VAR system are shown in Table 4.1 and 4.2

respectively. As evident from Table 4.1, Granger causality runs from doil(+) to inflation,

doil(+) to foreign exchange reserve, doil(-) to inflation, O(-) to inflation, O(-) to interest

rate, NOPI to inflation and NOPD to exchange rate. Results suggest the presence of

asymmetric impact of oil price shocks on economy. As shown in Table 4.1, negative oil

price shocks have much stronger impact on inflation compared to positive shocks. The

impact on foreign exchange reserve is prominent when there is an increase in real oil

price. The significant impact on inflation due to the negative oil price shocks is the result

of increase in demand. The higher inflationary pressure is then adjusted by the increase in

interest rates as a measure contractionary monetary policy. Like the linear case, global oil

price changes remain uninfluenced by any changes in domestic variables as shown in

Table 4.2

Table 5 presents GFEV for different macroeconomic variables attributable to

asymmetric oil price shocks. Like the linear one, most of FEV in oil price shocks have

been explained by its own shocks. One can observe the differential effects of asymmetric

specifications of oil price shocks, where negative price shocks have more pronounced

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effect on macroeconomic variables than positive shocks. For example, doil(-) accounts

for 2.5 percent of variance of inflation in 6 th and 12th months, while doil(+) explain only

1.9 and 1.6 percent of fluctuations for the same period. Similarly, for foreign exchange

reserve, NOPD explains 5.6 percent of variations while NOPI explains only 2.1 percent

of variations at the end of 12th month.

Generalized impulse responses of macroeconomic variables due to one standard

error innovation of asymmetric oil price shocks are shown in Figures 2 to 7. It is evident

that irrespective of the nature off shocks, maximum impact is observed for inflation

though the nature of responses is different for different measure of oil price shocks. Due

to one unit S.E shock in doil(+), doil(-), O(-) and NOPI, inflation witnesses a sharp

positive rise, followed by interest rates and foreign exchange reserves. The impact on

inflation reaches its maximum value in the first month after the shock. There is a dip in

the second month followed by another peak in third month due to shock in doil(+), O(-)

and NOPI. After that, the influence starts declining and becomes very small and negative

after seven months. In the case of O(+), inflation reacts positively and reaches its peak at

second month. There is a sharp decline followed by a smaller positive peak in third and

fourth months respectively. For NOPD, inflation exhibits a negative response and reaches

its minima in first month. It then starts moving in a positive direction and comes back to

its pre-shock level after six months. Thus, other than NOPD, positive and negative oil

price shocks increase inflation and negative price shocks cause more inflation than

positive shocks. Interest rates show a positive increase and reaches its peak around

second or third month due to shocks in doil(+), doil(-), O(-) and NOPI and then stay at

the same level for next few months. Foreign exchange reserves show an upward trend for

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most of the asymmetric shocks from the second and third month and then go down. The

impact on foreign exchange reserve is most prominent in doil(+). Impact of various

asymmetric specifications of oil price shocks on other macroeconomic variables is mild.

Granger causality tests, forecast error variance decomposition analysis and the

impulse response function analysis suggest that the oil price shocks have asymmetric

effects in the Indian economy. The economy responds more to the negative price shocks

than the positive one. Unlike the economies where oil price movements are linked with

the market, positive oil price shocks fail to impact the macro fundamentals like IIP,

exports, exchange rate in the Indian economy. The impact on inflation and foreign

exchange reserves due to positive oil price shocks is the resultant of government

borrowings where foreign exchange reserves are used as one of the internal source of

fund. The increasing trend in inflation due to negative oil price shocks could possibly be

explained by the fact that the government responds more to positive oil price shocks than

that of negative shocks to prevent adverse consequences in the economy. The negative oil

price shocks result in the increase in aggregate demand which builds pressure in inflation.

To stabilize the liquidity in the system and to control inflation, central banks apply

indirect monetary policy measures by increasing the policy interest rates moderately.

5. Conclusion

The study investigates the dynamic impact of linear and various non-linear

specifications of oil price shocks on some macroeconomic variables for an oil importing

developing country India for the data span March 1991 to January 2009. The study

employs TY extended VAR model, which can be used irrespective of the order of

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integration of underlying variables and irrespective of whether the variables are

cointegrated. The study establishes unidirectional causality running from oil price shocks

to inflation and oil price shocks to foreign exchange reserves in the case of linear model.

In case of non-linear models, Granger causality is found to be running from real oil price

increase to inflation, real oil price increase to foreign exchange reserve, real oil price

decrease to inflation, negative oil price volatility to inflation, negative oil price volatility

to interest rate, net oil price increase to inflation and net oil price decrease to exchange

rate. Granger causality also reveals that oil price shocks fail to affect Indias IIP and

export irrespective of linear or non-linear specifications.

The study also examines generalized error variance decomposition and impulse

response paths of macroeconomic variables due to oil price shocks. The study confirms

that oil price is exogenous with respect to Indias macro-economy and impacts of oil

price shocks are asymmetric in nature with negative price shocks having more

pronounced effect on inflation than positive shocks.

In India, as mentioned earlier, the price of petroleum products is insulated from

the volatility of international crude oil price. Hence, the movements in oil price shocks

dont have any major macroeconomic impact in Indian scenario. However, as the study

suggests, governments intension to curb inflation by insulating domestic petroleum price

from international crude price fluctuation is ineffective as inflation anyway happens due

to international oil price fluctuations through some other routes. The impact on inflation

due to positive oil price shocks could be explained via the channel of worsening fiscal

deficit due to oil subsidy. Following the seminal contribution by Sargent and Wallace

(1981) it is viewed that fiscally dominant governments running persistent deficits would

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sooner or later finance those deficits through creation of money, which will have

inflationary consequences. Khundrakpam and Goyal (2009) found that government

deficit continues to be a key factor causing incremental reserve money creation and

overall expansion in money supply, which lead to inflation in India.

The economy responds more to the negative price shocks than the positive one.

The increasing trend in inflation due to negative oil price shocks could possibly be

explained by the fact that the government responds more to positive oil price shocks than

that of negative shocks to prevent adverse consequences in the economy. The negative oil

price shocks result in the increase in inflation due to aggregate demand and supply gap.

To stabilize the liquidity in the system, central banks apply its effective tool of

controlling inflation by increasing the policy interest rates moderately.

This study supports that price of petroleum products must be market-determined.

This will reduce market distortions and open ended consumption subsidy and hence ease

out fiscal deficit. However, India needs to minimize the supply and price risks associated

with imported fuel. Import dependence can be reduced by substituting imported fuels

with domestic fuels like biodiesel and ethanol and electrification of railways. It has been

estimated that if all goods traffic were carried by Railways using electric traction, the

diesel saved would have been around 8 Mt in 2003-04 (IEP). Focus should also be given

to develop indigenous energy sources like gas hydrates, shell gas, coal-bed methane,

underground coal gasification, wind, solar, nuclear energy etc to prevent the negative

macroeconomic consequences of oil price fluctuations, which is expected to rise further

with the recent unrest in oil-reach countries.

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Figure Captions

Figure 1: Generalized impulse response due to one S.E. shock on doil (linear)

Generalized impulse response due to one S.E. shock in oil price (asymmetric)

Figure 2: IRF due to one unit SE shock on doil(+)

Figure 3: IRF due to one unit SE shock on doil(-)

Figure 4: IRF due to one unit SE shock on O(+)

Figure 5: IRF due to one unit SE shock on O(-)

Figure 6: IRF due to one unit SE shock on NOPI

Figure 7: IRF due to one unit SE shock on NOPD

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Figure 1

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Figure 2

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

1 2 3 4 5 6 7 8 9 10 11

ex

iip

inf

int

exp

forex

Figure 3

-5.00%

0.00%

5.00%

10.00%

15.00%

20.00%

1 2 3 4 5 6 7 8 9 10 11

ex

iip

inf

int

exp

forex

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Figure 4

-8.00%

-6.00%

-4.00%

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

1 2 3 4 5 6 7 8 9 10 11

ex

iip

inf

int

exp

forex

Figure 5

-4.00%

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

18.00%

1 2 3 4 5 6 7 8 9 10 11

ex

iip

inf

int

exp

forex

Figure 6

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-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

1 2 3 4 5 6 7 8 9 10 11

ex

iip

inf

int

exp

forex

Figure 7

-10.00%

-8.00%

-6.00%

-4.00%

-2.00%

0.00%

2.00%

1 2 3 4 5 6 7 8 9 10 11 ex

iip

inf

int

exp

forex

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Table 1. Unit root tests

---------------------------------------------------------------------------------------------------------------------

ADF PP

KPSS

---------------------------------------------------------------------------------------------------------------------

Level (Constant, trend)

oil -2.68 -2.34 0.38*

ex -2.76 -2.62 0.32*

iip -2.74 -8.76* 0.15*

inf -7.15* -9.65* 0.03

int -4.73* -6.60* 0.09

exp -1.57 -4.11* 0.36*

forex -2.23 -2.61 0.17*

Level (Constant, no trend)

oil -1.97 -1.73 1.16*

ex -2.93a -2.83a 0.35*

iip -0.65 -0.37 1.95*

inf -5.67* -9.67* 0.04

int -2.80 -5.01* 1.12*

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exp 0.03 -0.65 1.79*

forex -1.09 -1.41 1.89*

First-difference (Constant, no trend)

oil -10.93* -10.82* 0.06

ex -13.27* -13.30* 0.27

iip -3.22* -36.53* 0.06

inf -8.92* -36.04* 0.06

int -11.07* -41.80* 0.50a

exp -3.51* -38.39* 0.17

forex -3.79* -15.83* 0.12

---------------------------------------------------------------------------------------------------

aHo accepted at 1% level only

*H0 is rejected

Table 2. Granger causality tests (linear)

Null Hypothesis of non-causality; 2(2) statistics

doil ex iip inf int forex

doil 1.6072

(0.448)

1.1118

(0.574)

17.8708*

(0.000)

2.7253

(0.256)

17.4688*

(0.000)

ex 1.6973

(0.428)

NOT THE FOCUS AREA

iip 2.4990

(0.287)

inf 0.4449

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(0.801)

int 1,5174

(0.468)

forex 0.41297

(0.813)

-------------------------------------------------------------------------------------------------------------------------------------------

Probability Values in parenthesis

*implies statistical significance

Table 3 Generalised Forecast Error Variance Decomposition (linear)

Horizon doil ex iip inf int exp forex

1 97.8% 0.4% 1.0% 0.7% 0.2% 1.2% 2.0%

6 90.4% 1.3% 1.4% 1.9% 1.2% 1.8% 4.6%

12 89.8% 1.4% 1.5% 1.9% 1.4% 1.8% 4.6%

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Table 4.1. Granger causality tests from asymmetric oil price shocks to

Macroeconomic variables

2(2) statistics

-------------------------------------------------------------------------------------------------------------------------------------------------------------------

Figures in parenthesis represent probability values

*implies statistical significance

31

ex iip inf int exp forex

doil(+)

0.50432

(0.777)

0.2186

(0.896)

7.6481*

(0.022)

1.81

(0.405)

0.6772

(0.713)

32.2158*

(0.000)

doil(-)

2.3297

(0.312)

3.6162

(0.164)

21.3045*

(0.000)

3.088

(0.214)

4.6922

(0.096)

2.0475

(0.359)

O(+)

0.0562

(0.972)

0.0163

(0.992)

4.5111

(0.105)

0.0043

(0.998)

4.5595

(0.102)

2.5865

(0.274)

O(-)

0.4708

(0.79)

3.4069

(0.182)

17.969*

(0.000)

6.5981*

(0.037)

3.8725

(0.144)

3.6118

(0.164)

NOPI

0.4299

(0.807)

3.2107

(0.201)

14.7362*

(0.001)

1.3008

(0.522)

1.2962

(0.523)

0.4389

(0.803)

NOPD

12.4605*

(0.002)

1.0494

(0.592)

2.5514

(0.279)

0.1986

(0.905)

3.2437

(0.132)

0.1588

(0.924)

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Table 4.2. Granger causality tests from macroeconomic variables to asymmetric oil

price shocks

2(2) statistics

doil(+) doil(-) O(+) O(-) NOPI NOPD

ex

3.4613

(0.177)

0.4153

(0.812)

0.7575

(0.685)

2.5007

(0.286)

0.4783

(0.787)

0.3800

(0.827)

iip

1.478

(0.478)

2.1775

(0.337)

0.5818

(0.748)

3.3634

(0.186)

1.6261

(0.443)

4.1264

(0.127)

inf

2.4197

(0.298)

0.1820

(0.913)

1.8616

(0.394)

0.3098

(0.856)

5.3337

(0.086)

1.6607

(0.436)

int

1.4558

(0.483)

0.7705

(0.68)

0.7712

(0.68)

0.9735

(0.615)

2.9939

(0.224)

0.1176

(0.943)

exp

2.8433

(0.241)

1.4626

(0.481)

0.7210

(0.697)

3.5519

(0.169)

2.8475

(0.241)

1.8997

(0.387)

for

0.2469

(0.884)

1.0175

(0.601)

2.5565

(0.279)

4.1139

(0.128)

1.9306

(0.381)

3.774

(0.152)

---------------------------------------------------------------------------------------------------------------------------------------------------------------

Figures in parenthesis represent probability values

Table 5 Forecast Error Variance Decomposition (asymmetric)

Horizon doil ex iip inf int exp forex

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Variance decomposition of doil(+)

1 97.90% 1.24% 0.53% 0.66% 0.01% 0.38% 0.10%

6 91.02% 2.63% 1.92% 1.90% 1.53% 1.22% 0.15%

12 90.22% 2.67% 2.51% 1.90% 1.57% 1.23% 0.20%

Variance decomposition of doil(-)

1 98.53% 0.15% 1.01% 1.80% 0.08% 1.87% 2.45%

6 95.64% 0.32% 1.07% 2.52% 0.86% 1.88% 2.47%

12 95.37% 0.45% 1.07% 2.53% 0.96% 1.91% 1.57%

Variance decomposition of O(+)

1 98.92% 0.21% 1.29% 1.41% 0.60% 0.22% 0.26%

6 95.21% 0.61% 1.96% 2.30% 0.67% 0.81% 1.54%

12 94.84% 0.64% 2.01% 2.29% 0.66% 1.10% 1.59%

Variance decomposition of O(-)

1 96.30% 0.58% 1.80% 0.61% 0.13% 0.78% 2.93%

6 92.44% 1.40% 1.86% 1.16% 0.41% 1.64% 3.53%12 92.25% 1.43% 1.93% 1.16% 0.47% 1.67% 3.54%

Variance decomposition of NOPI

1 96.96% 0.09% 2.90% 0.59% 1.39% 0.44% 1.72%

6 93.39% 0.54% 2.88% 2.06% 1.67% 1.05% 2.00%

12 92.86% 0.65% 2.87% 2.10% 1.84% 1.10% 2.15%

Variance decomposition of NOPD

1 97.21% 0.81% 3.86% 0.73% 0.52% 0.72% 1.34%

6 89.07% 1.03% 4.52% 1.38% 0.68% 1.88% 4.85%

12 87.26% 1.10% 5.27% 1.35% 0.83% 2.03% 5.60%

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