Global Commodity and Oil Price Movements,Macroeconomic Performance and Challenges for an Emerging Economy: The Indian Experience

Abstract

Commodity and oil price fluctuations have significant bearing on domestic macroeconomic performance and macroeconomic policymaking of an emerging economy. The article explores the impact of non-energy commodity and oil price fluctuations on output, inflation and real exchange rate (RER) in India; and commodity and oil constituting sizeable imports. The empirical analysis carried out through vector error correction model (VECM) for the post-liberalization period 1991–2014 clearly points out that commodity and oil price shocks have a significant impact on the variation in output and prices accounting for RER adjustment and the role of a developed financial market (private credit). The RER adjusts to commodity and oil price shocks, accounting for foreign exchange reserves and financial markets (private credit). The impulse response functions indicate that one standard deviation shock in commodity and oil price persists for three to eight quarters over domestic prices and output. While these results point to lessening of commodity and oil imports through a series of medium and long-term structural-cum-policy reform measures, in the immediate, they also lend a role of intervention by monetary authority (central bank) in pursuit of inflation targeting. Conjointly, pursuance of countercyclical fiscal policy to stabilize domestic output and prices in short run are called for.

Keywords

Commodity price oil price output variation real exchange rate vector error correction model impulse response function central bank intervention

Introduction

Global commodity and oil price fluctuations have increased over the years. UNCTAD Handbook of Statistics (2009) data show that commodity price volatility has increased by 175 per cent over the last two decades. Fluctuations in global commodity and oil price impact domestic macroeconomic performance of emerging economies. While global oil and commodity price boom might significantly benefit commodity- and oil-exporting countries, but a sharp fall in commodity and oil price equally increase macroeconomic vulnerability; wiping out real incomes and welfare. Similarly, with rising commodity prices, large commodity- and oil-importing countries face significant macroeconomic stress with resultant output fluctuation, inflation and fiscal duress. Thus, the governments are bound to implement measures first to cope with commodity and oil price volatility, and then, to maintain production and price incentives to stabilize the economy. India, as a large oil and commodity importing emerging economy is no exception to this.

Figure 1 to Figure 4, plot quarterly rate of change in global commodity and oil price with rate of change in domestic output (industrial production) and inflation. A cursory glance through Figure 1 to Figure 2 suggests that, in general, global commodity and oil price movements have a lead-lag inverse relationship with industrial production. The swings in industrial output appear to be more pronounced than the rate of change in commodity and oil price. It is discernible from Figure 3 and Figure 4 that the rate of domestic wholesale price inflation is closely associated with commodity and oil price movements, also swings in domestic inflation rate is larger than commodity and oil price movements. Moreover, since 2008, quarterly fluctuations in domestic inflation rate appear to be following more closely to commodity and oil price movements. Given these cursory observations, we briefly review underlying open-economy macroeconomic interrelationships among commodity and oil price output, inflation and exchange rate. This is to set the research agenda at the end of the second section.

Review of Literature

While the degree of impact of commodity and oil price shocks depends on country-specific structural characteristics, countries that are typically adversely affected are usually countries with high net imports of commodity and oil per GDP. The spikes in crude oil price significantly increase the energy cost of domestic industries resulting in cost-push inflation (Ekong & Effiong, 2015). Whereas, in markets with nominal price and wage rigidities, a delayed price adjustment can even create a bigger output shock in the long run. During the times of sharp worsening terms of trade, macroeconomic performance is contingent on the degree of flexibility in exchange rate management given the state of external balance sheet and fiscal excesses. A steep fall in current account and consequent worsening of fiscal deficit, in turn, is reflected in saving-investment imbalances and economic crises. Macroeconomic transmission of terms of trade shocks are generally through significant variance in output. Nominal wage and price rigidities increase output volatility and unemployment. In a flexible exchange rate, nominal exchange rate immediately adjusts itself insulating the economy from the external shock and stabilizing output. Whereas, in a fixed exchange rate, terms of trade shocks are amplified with asymmetric response of output, that is, larger output response to negative than to positive shock (UNCTAD, 2012). In case of an output gap, the shocks will have an adverse impact on growth mainly through inflation and fall in real income. Further, an excessive volatility that creates uncertainty over future price levels discourages long-term planning and investment; derails growth and disrupts government budgets (Cespedes & Velasco, 2012).

Commodity and oil price boom and bust, therefore, pose a challenge for macroeconomic policy and thus warrant deft policy manoeuvring. While countries with managed exchange rates combined with capital controls might be able to avoid potential cyclicality from capital flows, others are successful in insulating the economy from terms of trade shocks through inflation-targeting combined with floating exchange rates. In managed exchange rate coupled with capital controls central bank intervention with varying foreign exchange reserves might be able to avoid potential risks of transmitting commodity price shocks to real exchange rate (RER; Aizenman, Edwards, & Riera-Crichton, 2011). Therefore, rather than raising interest rates, anchoring inflationary expectations turns out to be the key monetary policy objective wherein the headline inflation being the target and core inflation being the guidepost. On the other hand, countercyclical fiscal policy may complement monetary policy objective of containing commodity price shocks especially under an inflation targeting regime.

Bruno (1982) finds empirical evidence that oil price shocks lead to an increase in wages and prices, and decrease in real output. The same conclusion was substantiated by Hamilton (1983) using the Vector Autoregression (VAR) technique. Burbridge and Harrison (1984) found that the impact was different across different countries in spite of the fact that all were developed countries. On the other hand, Hooker (1996) found that the causal relationship between oil prices and macroeconomic variables weakened post-1973 and were not able to capture the dynamics of business. Whereas, Cristini (1998) observed a very strong correlation between macroeconomic variables and oil prices. Analysing the periods of commodity boom and bust for commodity exporting countries, Cespedes and Velasco (2012) found that commodity price booms are positively associated with heightened economic activity. Further, commodity terms of trade have a significant impact on RER but reserve accumulation contains the impact. More flexible exchange rate regime countries are associated with more moderate variation in output.

In the Indian context, most of the earlier studies (Rangarajan, Sah, & Reddy, 1981; Sastry, 1982, pp. 68–93) estimate the cost-push effect of oil price hike using input output analysis. This method is not useful in estimating the oil price shocks over a longer period of time given its static nature. In February 1999, from an all-time low of $11 per barrel, the oil prices increased to a peak of $35 in the first week of September 2000. From its peak of $147 during 2008 it has fallen to less than $50 at present. India imports more than 100 million tons of crude oil and other petroleum products, in turn spends large amounts of foreign exchange. The increasing quantum of imports of petroleum products has a significant impact on the Indian economy in times of soaring crude oil prices. It is estimated that for every unit dollar increase in crude oil price, wholesale price inflation rises by 30 basis points (Reserve Bank of India (RBI), 2005). These developments provide a motivation to understand the mechanisms through which commodity and oil price fluctuations are transmitted to the macroeconomy.

Objectives

Against these developments, we explore the oil and non-energy commodity price fluctuations and their impact on Indian macroeconomy. We provide the responses of different macroeconomic variables to oil and non-energy commodity price changes—different channels through which oil and commodity price changes affect an open-economy. Our objective is to find out the output response to oil and non-energy commodity price fluctuations. As outlined earlier, in flexible exchange rate, exchange rate adjustment or in a managed exchange rate variation, foreign exchange reserves (as a measure for degree of foreign exchange intervention) stabilize output response and hence output variation would smaller. Since India follows managed exchange rate wherein central bank actively intervened, in the past, in foreign exchange market to contain excess volatility in exchange rate, we also examine the exchange rate adjustment to a commodity shock along with variation in foreign exchange reserves. This is in order to examine whether variation in foreign exchange reserves contains exchange rate volatility in the wake of oil and non-oil energy commodity price shocks. Also, since well-developed financial markets reduce the impact of commodity and price shocks on credit and investment, we also examine the role of financial market (private credit as a percentage of GDP) in mitigating price shocks.

Database and Methodology

Fluctuations in crude oil prices impact the economy through various channels. We examine the response of industrial output to the episodic change in commodity and oil prices. We empirically establish different channels through which shocks to commodity and oil prices affect the domestic macroeconomy. We restrict this study to analyse the direct impact of oil and non-energy commodity prices on industrial output and domestic inflation, and thereby, on the growth of output. Finally, we provide an empirical account of the role played by the development of financial markets (private credit). Moreover, we analyse the impact of oil and non-energy commodity price fluctuations on RER adjustment while accounting for variation in foreign exchange reserves.

We use quarterly data for the post-reform period spanning over 1991 to 2014. In the absence of high-frequency quarterly data of GDP for the entire period, we use industrial output as a proxy for domestic output and we make use of Index of Industrial Production (IIP) to measure the variation in industrial output. The data for IIP has been sourced from the Ministry of Statistics and Programme Implementation, Government of India (GoI). For measuring domestic inflation rate we make use of Wholesale Price Index (WPI) and the WPI data has been obtained from the Ministry of Finance, GoI. As an indicator of financial market development of the economy, the share of credit to private sector of the total credit has been considered. The data related to credit to private sector has been taken from World Bank Economic Database. The data on RER represented by real effective exchange rate (REER) and data on foreign exchange reserves has been obtained from Database of Indian Economy managed by RBI.

To measure commodity price movements, the data on Commodity Price Index (CPI) has been obtained from the World Bank Economic Database (Figure 7). CPI includes non-energy commodities only, that includes agriculture (beverages, food and raw materials), fertilizers and metals and minerals (base metals). The composition of CPI is presented in Figure 5.

The crude oil price has been obtained from Economic Research Department of Federal Reserve Bank of St. Louis (Figure 8). Since we have observed seasonality in the quarterly data of IIP and WPI, we have deseasonalized IIP and WPI before carrying out empirical estimation (Figure 6 and 9).

Empirical Estimation Method

Following diagnosing standard time series properties of macroeconomic variables, unit roots test are carried out for all the variables and it is found that all the variables are non-stationary at levels but become stationary in their first differences. Vector error correction models (VECMs) are more appropriate for analysing such multivariate time series data as they are extremely useful in analysing the dynamic behaviour of economic and financial time series. Also, VECM models are superior over other causal time series models. Moreover, since we analyse the impact of commodity and oil price fluctuations on domestic macroeconomic variables of output, inflation and exchange rate, VECM are amenable to derive macroeconomic policy implications from the empirical findings. VECM is a multi-equation system where all the variables are treated as endogenous. The k-variable VAR model is given as:

_t₁_t–1₂_t–2_k_s–k_t

where

Y_t = (y_1t, y_2t,……y_m)

is a (nx1) vector of time series variables.

β_i(i = 1,2,….k) are n x n coefficient matrices. The right hand side of each equation includes lagged values of all dependent variables in the system.

ε_t is the error term which is a nx1 matrix.

Although Granger-causal relationship can also be established between the variables considered, however VECM is preferred over the former. The testable hypothesis proposed here are as follows:

There is a significant relationship between change in output, credit to private sector and crude oil price.

There is a significant relationship between change in output, credit to private sector and non-energy commodity price.

There is a significant relationship between change in REER, crude oil price, foreign exchange reserves and credit to private sector.

There is a significant relationship between change in REER, commodity price, foreign exchange reserves and credit to private sector.

Empirical Results

a. The Schwarz Information Criterion (SIC) is the least for a lag of five therefore indicating that a lag of five period is the optimal. Further, both trace and maximum eigenvalue test indicate one co-integrating equation (Table 1 and 2). The high value of Adjusted R-squared for Δ(IIP) and other variables indicates a good fit between these variables. The F-statistics also indicates a significant relationship. Therefore, we may infer that there exists a significant relationship between the IIP, crude oil price and credit to private sector as evident from Table 3.

Table 1.

Unrestricted Cointegration Rank Test (Trace)

Hypothesized No. of CE(s)	Eigenvalue	Trace	0.05	Prob.**
Hypothesized No. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**
None *	0.263419	57.35116	47.85613	0.0050
At most 1	0.192363	28.91776	29.79707	0.0629
At most 2	0.090288	9.049048	15.49471	0.3609
At most 3	0.002670	0.248679	3.841466	0.6180

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

Notes: Trace test indicates 1 cointegrating eqn(s) at the 0.05 level.

* denotes rejection of the hypothesis at the 0.05 level.

** MacKinnon–Haug–Michelis (1999) p-values.

Table 2.

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized No. of CE(s)	Eigenvalue	Max. Eigen	0.05	Prob.**
Hypothesized No. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**
None *	0.263419	28.43340	27.58434	0.0389
At most 1	0.192363	19.86871	21.13162	0.0743
At most 2	0.090288	8.800369	14.26460	0.3031
At most 3	0.002670	0.248679	3.841466	0.6180

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

Notes: Max. eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level.

* denotes rejection of the hypothesis at the 0.05 level.

** MacKinnon–Haug–Michelis (1999) p-values.

Table 3.

Cointegration of Oil Prices, Credit, WPI and IIP

Cointegrating Equation	Cointegrating Equation 1
IIP (–1)	1.000000
OIL (–1)	–0.962819 (2.70528) [–0.35590]
WPI (–1)	–2.479277 (0.48956) [–5.06434]
CREDIT (–1)	30.92398 (12.7514) [2.42515]
C	–359.8611

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

From the aforementioned equation, we can see that increase in crude oil prices has an inflationary impact on the economy. We can also see that the increase in crude oil prices has a negative impact on industrial production (output). Inflationary impact shows with a lag of one quarter while the impact on output is quite delayed with a lag of three quarters. Further, the Wald-test reinforces underlying macroeconomic interrelationship, that there exists a short run relationship between change in output and oil prices and WPI and oil prices.

Table 4.

VECM of Oil Prices, Credit, WPI and IIP

Error Correction	Δ(IIP)	Δ(OIL)	Δ(WPI)	Δ(CREDIT)
CointEq1	0.039485 (0.01227) [3.21755]	0.007832 (0.01186) [0.66044]	0.028610 (0.00614) [4.65774]	0.000359 (0.00129) [0.27702]
Δ(IIP (–1))	–0.393382 (0.13633) [–2.88556]	–0.102069 (0.13173) [–0.77483]	–0.112086 (0.06824) [–1.64261]	0.029259 (0.01439) [2.03401]
Δ(IIP (–2))	–0.347111 (0.09096) [–3.81627]	0.106489 (0.08789) [1.21163]	–0.111064 (0.04553) [–2.43957]	0.013874 (0.00960) [1.44558]
Δ(IIP (–3))	–0.368176 (0.09315) [–3.95262]	0.016298 (0.09001) [0.18108]	–0.121967 (0.04662) [–2.61599]	0.012392 (0.00983) [1.26076]
Δ(IIP (–4))	0.654599 (0.09699) [6.74925]	0.049963 (0.09372) [0.53312]	–0.105783 (0.04855) [–2.17903]	0.001339 (0.01023) [0.13080]
Δ(IIP (–5))	0.101673 (0.12456) [0.81624]	0.197359 (0.12036) [1.63969]	–0.037604 (0.06235) [–0.60312]	–0.011460 (0.01314) [–0.87189]
Δ(OIL (–1))	0.078987 (0.16032) [0.49268]	0.461773 (0.15492) [2.98078]	0.362617 (0.08025) [4.51878]	0.006065 (0.01692) [0.35850]
Δ(OIL (–2))	–0.247986 (0.17315) [–1.43216]	–0.285008 (0.16732) [–1.70340]	0.001305 (0.08667) [0.01506]	0.031234 (0.01827) [1.70948]
Δ(OIL (–3))	–0.423569 (0.18175) [–2.33052]	0.108124 (0.17562) [0.61567]	–0.000786 (0.09097) [–0.00864]	0.026512 (0.01918) [1.38244]
Δ(OIL (–4))	–0.262483 (0.17407) [–1.50790]	–0.366362 (0.16820) [–2.17810]	–0.000423 (0.08713) [–0.00485]	0.021834 (0.01837) [1.18872]
Δ(OIL (–5))	–0.288096 (0.16339) [–1.76319]	–0.130156 (0.15789) [–0.82437]	–0.128581 (0.08178) [–1.57220]	0.020513 (0.01724) [1.18977]
Δ(WPI (–1))	–0.533867 (0.28322) [–1.88498]	–0.235199 (0.27367) [–0.85942]	0.107204 (0.14176) [0.75623]	–0.033905 (0.02989) [–1.13450]
Δ(WPI (–2))	0.050293 (0.30659) [0.16404]	–0.472216 (0.29625) [–1.59396]	–0.162662 (0.15346) [–1.05997]	0.009351 (0.03235) [0.28905]
Δ(WPI (–3))	0.238405 (0.29400) [0.81090]	0.009978 (0.28409) [0.03512]	0.127064 (0.14716) [0.86346]	–0.042914 (0.03102) [–1.38331]
Δ(WPI (–4))	–0.861681 (0.29765) [–2.89496]	0.489341 (0.28761) [1.70139]	0.161437 (0.14898) [1.08359]	0.000917 (0.03141) [0.02918]
Δ(WPI (–5))	0.013007 (0.31429) [0.04138]	–0.253439 (0.30369) [–0.83453]	–0.141230 (0.15731) [–0.89777]	–0.003845 (0.03316) [–0.11593]
Δ(CREDIT (–1))	0.330886 (1.30646) [0.25327]	–0.192898 (1.26241) [–0.15280]	–1.257493 (0.65393) [–1.92299]	–0.200914 (0.13786) [–1.45742]
Δ(CREDIT (–2))	0.281949 (1.28834) [0.21885]	–1.339440 (1.24490) [–1.07594]	–1.108359 (0.64486) [–1.71876]	–0.225432 (0.13594) [–1.65827]
Δ(CREDIT (–3))	0.676888 (1.29317) [0.52343]	0.409109 (1.24957) [0.32740]	–0.500149 (0.64728) [–0.77270]	–0.152487 (0.13645) [–1.11749]
Δ(CREDIT (–4))	–1.758102 (1.21599) [–1.44582]	0.365215 (1.17499) [0.31082]	–0.654463 (0.60865) [–1.07528]	–0.130280 (0.12831) [–1.01535]
Δ(CREDIT (–5))	–0.849381 (1.23466) [–0.68795]	0.175298 (1.19304) [0.14693]	–1.814763 (0.61799) [–2.93655]	–0.063193 (0.13028) [–0.48505]
C	19.32270 (4.94659) [3.90627]	2.749648 (4.77981) [0.57526]	11.39602 (2.47593) [4.60272]	0.669507 (0.52196) [1.28269]
R-squared	0.937280	0.460536	0.578621	0.401305
Adj. R-squared	0.914956	0.268523	0.428638	0.188210
Sum sq. resids	5,732.908	5,352.848	1,436.287	63.83124
S.E. equation	9.857379	9.525033	4.933948	1.040137
F-statistic	41.98510	2.398467	3.857925	1.883222

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

The VECM estimates can be summarized in the following equation:

Δ(IIP) = 0.039485*(IIP (–1) – 0.962818895106*OIL (–1) – 2.47927721085*WPI (–1) + 30.9239790826*CREDIT (–1) – 359.861106415) – 0.393382*Δ(IIP (–1)) – 0.347111*Δ(IIP (–2)) – 0.368176*Δ(IIP (–3)) + 0.654599* Δ(IIP (–4)) – 0.423569*Δ(OIL (–3)) – 0.288096*Δ(OIL (–5)) – 0.533867*Δ(WPI (–1)) – 0.861681* Δ(WPI (–4)) + 19.32270

Δ(WPI) = 0.028610*(IIP (–1) – 0.962818895106*OIL (–1) –2.47927721085*WPI (–1) + 30.9239790826*CREDIT (–1) – 359.861106415) – 0.111064*Δ(IIP (–2)) – 0.121967*Δ(IIP (–3)) – 0.105783*Δ(IIP (–4)) + 0.362617*Δ(OIL (–1)) – 1.257493*Δ(CREDIT (–1)) – 1.108359*Δ(CREDIT (–2)) – 1.814763*Δ(CREDIT (–5)) + 11.39602

The impulse response functions (Figure 10 and 11) indicate that the impact of oil price shocks on IIP lasts up to four quarters after which the effect becomes constant. We can also observe that oil price shocks have a positive impact on WPI pointing out the domestic inflationary effect. The impact becomes constant from eight-quarter onwards.

b. The SIC and the Hannan–Quinn information criterion is least for a lag of five therefore indicating that a lag of five periods is the optimal. Further, trace test and maxium eigenvalue test indicates three cointegrating equations (Table 4, 5, 6 and 7). The high value of Adjusted R-Squared for Δ(IIP) and other variables indicates a good fit among these variables. The F-statistics also indicates a significant relationship. Therefore, we may infer that there exists a significant relationship between the change in output, non-energy commodity prices, WPI and credit to private sector as indicated in Table 7.

From the aforementioned VECM equation, we observe that increase in commodity prices has an inflationary impact on the economy (Table 8). The increase in commodity prices has a negative impact on output. Inflationary impact comes with a lag of one quarter while the negative impact on output is quite delayed with a lag of three quarters. Further, the Wald-test reinforces underlying macroeconomic theory that there exists a short run relationship between output and commodity prices and WPI and commodity prices.

Table 5.

Unrestricted Cointegration Rank Test (Trace)

Hypothesized No. of CE(s)	Eigenvalue	Trace	0.05	Prob.**
Hypothesized No. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**
None *	0.338670	80.25384	47.85613	0.0000
At most 1 *	0.296801	46.76014	29.79707	0.0002
At most 2 *	0.179504	18.23879	15.49471	0.0188
At most 3	0.026954	2.213271	3.841466	0.1368

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

Notes: Trace test indicates 3 cointegrating eqn(s) at the 0.05 level.

* denotes rejection of the hypothesis at the 0.05 level.

** MacKinnon–Haug–Michelis (1999) p-values.

CE: cointegrating equations.

Table 6.

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized No. of CE(s)	Eigenvalue	Max. Eigen	0.05	Prob.**
Hypothesized No. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**
None *	0.338670	33.49369	27.58434	0.0077
At most 1 *	0.296801	28.52136	21.13162	0.0038
At most 2 *	0.179504	16.02552	14.26460	0.0261
At most 3	0.026954	2.213271	3.841466	0.1368

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

Notes: Max. eigenvalue test indicates 3 cointegrating eqn(s) at the 0.05 level.

* denotes rejection of the hypothesis at the 0.05 level.

** MacKinnon–Haug–Michelis (1999) p-values.

CE: cointegrating equations.

Table 7.

Cointegration of Commodity Prices, Credit, WPI and IIP

Cointegrating Equation	Cointegrating Equation 1	Cointegrating Equation 2	Cointegrating Equation 3
IIP (–1)	1.000000	0.000000	0.000000
CPI (–1)	0.000000	1.000000	0.000000
WPI (–1)	0.000000	0.000000	1.000000
CREDIT (–1)	–15.21386 (0.84892) [–17.9214]	–3.189810 (0.59865) [–5.32835]	–16.53888 (1.27067) [–13.0159]
C	27.75052	44.59095	107.2058

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

Table 8.

VECM of Commodity Prices, Credit, WPI and IIP

Error Correction	Δ(IIP)	Δ(CPI)	Δ(WPI)	Δ(CREDIT)
CointEq1	–0.102486 (0.05492) [–1.86621]	0.025314 (0.02717) [0.93167]	0.110795 (0.03047) [3.63678]	0.012209 (0.00603) [2.02480]
CointEq2	–0.171633 (0.09053) [–1.89591]	–0.071143 (0.04479) [–1.58838]	0.036705 (0.05022) [0.73087]	–0.016831 (0.00994) [–1.69336]
CointEq3	–0.078628 (0.03350) [–2.34685]	–0.001147 (0.01658) [–0.06922]	–0.086848 (0.01859) [–4.67271]	0.000137 (0.00368) [0.03736]
Δ(IIP (–1))	–0.305643 (0.12924) [–2.36500]	0.010538 (0.06394) [0.16481]	–0.097159 (0.07169) [–1.35519]	0.005195 (0.01419) [0.36611]
Δ(IIP (–2))	–0.261084 (0.09435) [–2.76709]	0.089075 (0.04668) [1.90813]	–0.159831 (0.05234) [–3.05354]	–0.001509 (0.01036) [–0.14571]
Δ(IIP (–3))	–0.306654 (0.09261) [–3.31132]	0.053769 (0.04582) [1.17352]	–0.147825 (0.05137) [–2.87740]	0.001385 (0.01017) [0.13624]
Δ(IIP (–4))	0.679727 (0.09339) [7.27816]	0.149631 (0.04621) [3.23830]	–0.076039 (0.05181) [–1.46765]	–0.004536 (0.01025) [–0.44237]
Δ(IIP (–5))	0.107608 (0.12803) [0.84051]	0.070103 (0.06334) [1.10674]	–0.125277 (0.07102) [–1.76389]	–0.000332 (0.01406) [–0.02359]
Δ(CPI (–1))	0.858481 (0.27406) [3.13244]	0.453358 (0.13559) [3.34349]	0.606632 (0.15204) [3.99004]	0.013772 (0.03009) [0.45770]
Δ(CPI (–2))	–0.283217 (0.31388) [–0.90231]	–0.105150 (0.15529) [–0.67710]	–0.081644 (0.17413) [–0.46888]	0.094280 (0.03446) [2.73573]
Δ(CPI (–3))	–0.533421 (0.31670) [–1.68430]	–0.089233 (0.15669) [–0.56949]	0.053605 (0.17569) [0.30511]	–0.008340 (0.03477) [–0.23984]
Δ(CPI (–4))	–0.322145 (0.30087) [–1.07070]	–0.061575 (0.14886) [–0.41365]	0.051068 (0.16691) [0.30596]	0.045258 (0.03303) [1.37005]
Δ(CPI (–5))	–0.713313 (0.29900) [–2.38566]	0.155606 (0.14793) [1.05187]	0.053192 (0.16587) [0.32068]	0.061960 (0.03283) [1.88738]
Δ(WPI (–1))	–0.553586 (0.26795) [–2.06599]	–0.273820 (0.13257) [–2.06546]	–0.006350 (0.14865) [–0.04272]	–0.059212 (0.02942) [–2.01267]
Δ(WPI (–2))	0.183640 (0.29572) [0.62099]	–0.209412 (0.14631) [–1.43129]	–0.155767 (0.16405) [–0.94950]	–0.002531 (0.03247) [–0.07795]
Δ(WPI (–3))	0.447353 (0.27380) [1.63384]	0.021236 (0.13547) [0.15676]	–0.001113 (0.15189) [–0.00733]	–0.041972 (0.03006) [–1.39617]
Δ(WPI (–4))	–0.555818 (0.28386) [–1.95804]	–0.133657 (0.14044) [–0.95167]	–0.116086 (0.15747) [–0.73717]	–0.033946 (0.03117) [–1.08918]
Δ(WPI (–5))	0.349616 (0.26420) [1.32328]	0.027195 (0.13072) [0.20804]	–0.167520 (0.14657) [–1.14295]	–0.057456 (0.02901) [–1.98068]
Δ(CREDIT (–1))	–2.212601 (1.34468) [–1.64545]	–0.406087 (0.66529) [–0.61039]	–0.056815 (0.74597) [–0.07616]	–0.074652 (0.14764) [–0.50564]
Δ(CREDIT (–2))	–2.048196 (1.25471) [–1.63240]	–0.433297 (0.62078) [–0.69799]	–0.164042 (0.69605) [–0.23567]	–0.112413 (0.13776) [–0.81601]
Δ(CREDIT (–3))	–1.257878 (1.23929) [–1.01500]	–0.291816 (0.61315) [–0.47593]	–0.586778 (0.68750) [–0.85350]	–0.090279 (0.13607) [–0.66349]
Δ(CREDIT (–4))	–3.390627 (1.14051) [–2.97290]	–0.217743 (0.56428) [–0.38588]	–0.383342 (0.63270) [–0.60588]	–0.116717 (0.12522) [–0.93208]
Δ(CREDIT (–5))	–2.022628 (1.18476) [–1.70721]	–0.660968 (0.58617) [–1.12761]	–1.239094 (0.65725) [–1.88528]	–0.065036 (0.13008) [–0.49997]
C	13.09904 (5.56969) [2.35184]	2.340239 (2.75564) [0.84925]	14.97679 (3.08980) [4.84717]	1.785703 (0.61152) [2.92011]
R-squared	0.949618	0.569315	0.584213	0.479320
Adj. R-squared	0.929289	0.395529	0.416439	0.269221
Sum sq. resids	4,605.110	1,127.261	1,417.226	55.51352
S.E. equation	8.988404	4.447079	4.986343	0.986875
F-statistic	46.71155	3.275966	3.482145	2.281398

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

The VECM estimates can be summarized in the following equation:

Δ(IIP) = – 0.102486*(IIP(–1) – 15.2138626646*CREDIT(–1) + 27.7505172739) + –0.171633* (CPI(–1) – 3.18980969409*CREDIT(–1) + 44.590953894) – 0.078628* (WPI(–1) – 16.5388808415*CREDIT(–1) + 107.205810474) – 0.305643*Δ(IIP(–1)) – 0.261084*Δ(IIP(–2)) – 0.306654*Δ(IIP(–3)) + 0.679727*Δ(IIP(–4)) + 0.858481*Δ(CPI(–1)) – 0.533421*Δ(CPI(–3)) – 0.713313*Δ(CPI(–5)) – 0.553586*Δ(WPI(–1)) – 0.555818*Δ(WPI(–4)) – 3.390627*Δ(CREDIT(–4)) – 2.022628*Δ(CREDIT(–5)) + 13.09904

Δ(WPI) = 0.110795*(IIP(–1) – 15.2138626646*CREDIT(–1) + 27.7505172739) – 3.18980969409* CREDIT(–1) + 44.590953894)) – 0.086848*(WPI(–1) – 16.5388808415*CREDIT(–1) + 107.205810474) – 0.159831*Δ(IIP(–2)) – 0.147825*Δ(IIP(–3)) – 0.125277*Δ(IIP(–5)) + 0.606632*Δ(CPI(–1)) – 1.239094*Δ(CREDIT(–5)) + 14.97679

We can also observe that commodity price shock has a positive impact on WPI which points to the domestic inflationary effect and its impact lasts for three quarters. Whereas, its impact on output is negative and lasts longer.

c. The SIC, Hannan–Quinn Information Criterion, Akaike Information Criterion and final prediction error are the least for a lag of two, therefore, indicating that a lag of two periods is the optimal. Further, trace test indicates two co-integrating equations (Table 9 and 10). The high value of Adjusted R-squared for Δ(REER) and other variables indicates a good fit between these variables. The F-statistics also indicates a significant relationship. Therefore, we may infer that there exists a significant relationship between the REER, oil price, foreign exchange reserves and credit to private sector as indicated in Table 11.

From the aforementioned equation, we can observe that increase in oil prices has an impact on the rate of exchange rate adjustment (Table 12). We can see from the aforementioned equation that positive (increase) shock in oil prices has a depreciating impact on the REER, as expected given the heavy dependence of Indian economy on oil imports. However, the impact comes with a lag of two quarters, therefore, the monetary authority (central bank) gets a time window to minimize the adverse impact. Further, financial markets (credit to private sector) and foreign exchange reserves help in mitigating the negative impact of oil price spikes on REER.

Table 9.

Unrestricted Cointegration Rank Test (Trace)

Hypothesized No. of CE(s)	Eigenvalue	Trace	0.05	Prob.**
Hypothesized No. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**
None *	0.327976	67.65301	47.85613	0.0003
At most 1 *	0.243749	34.26626	29.79707	0.0143
At most 2	0.119491	10.79823	15.49471	0.2242
At most 3	0.001295	0.108836	3.841466	0.7415

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

Notes: Trace test indicates 2 cointegrating eqn(s) at the 0.05 level.

* denotes rejection of the hypothesis at the 0.05 level.

**MacKinnon–Haug–Michelis (1999) p-values.

CE: cointegrating equations.

Table 10.

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized No. of CE(s)	Eigenvalue	Max-Eigen	0.05	Prob.**
Hypothesized No. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**
None *	0.327976	33.38676	27.58434	0.0080
At most 1 *	0.243749	23.46803	21.13162	0.0230
At most 2	0.119491	10.68939	14.26460	0.1705
At most 3	0.001295	0.108836	3.841466	0.7415

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

Notes: Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level.

* denotes rejection of the hypothesis at the 0.05 level.

**MacKinnon–Haug–Michelis (1999) p-values.

CE: cointegrating equations.

Table 11.

Cointegration of REER, Oil Prices, Foreign Exchange Reserves and Credit

Cointegrating Equation:	Cointegrated Equation 1	Cointegrated Equation 2
REER(–1)	1.000000	0.000000
OIL(–1)	0.000000	1.000000
RESERVES(–1)	0.136725	0.354605
	(0.05424)	(0.13132)
	[2.52079]	[2.70022]
CREDIT(–1)	–1.576840	–6.365719
	(0.55566)	(1.34538)
	[–2.83778]	[–4.73155]
C	–63.14794	127.8455

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

The impulse response functions (Figure 12) indicate that the impact lasts up to eight quarters after which the effect tends to becomes constant.

d. The Akaike Information Criterion and final prediction error are the least for a lag of three therefore indicating that a lag of three periods is the optimal. Further, trace test indicates one co-integrating equation (Table 13 and 14). The high value of Adjusted R-squared for Δ(REER) and other variables indicates a good fit between these variables. The F-statistics also indicates a significant relationship. Therefore, we may infer that there exists a significant relationship between REER, commodity price, foreign exchange reserves and credit to private sector as indicated in Table 15.

Table 12.

VECM of REER, Oil Prices, Foreign Exchange Reserves and Credit

Error Correction	Δ(REER)	Δ(OIL)	Δ(RESERVES)	Δ(CREDIT)
CointEq1	–0.393169(0.08673)[–4.53325]	0.651572(0.26129)[2.49370]	0.141969(0.24458)[0.58045]	–0.063904(0.04146)[–1.54137]
CointEq2	0.090454(0.03849)[2.35033]	–0.386269(0.11594)[–3.33153]	–0.308563(0.10853)[–2.84306]	0.024333(0.01840)[1.32265]
Δ(REER(–1))	0.288864(0.11498)[2.51241]	–0.778764(0.34638)[–2.24830]	–0.000989(0.32424)[–0.00305]	0.031711(0.05496)[0.57697]
Δ(REER(–2))	0.407714(0.11662)[3.49615]	0.006254(0.35133)[0.01780]	0.241756(0.32887)[0.73511]	–0.005270(0.05575)[–0.09453]
Δ(OIL(–1))	0.001483(0.04198)[0.03532]	0.234126(0.12648)[1.85108]	0.019009(0.11840)[0.16055]	–0.014953(0.02007)[–0.74507]
Δ(OIL(–2))	–0.091516(0.04180)[–2.18939]	–0.144927(0.12593)[–1.15088]	–0.044480(0.11788)[–0.37734]	–0.007737(0.01998)[–0.38721]
Δ(RESERVES(–1))	0.037011(0.04048)[0.91418]	0.420266(0.12197)[3.44574]	0.646402(0.11417)[5.66174]	0.007203(0.01935)[0.37217]
Δ(RESERVES(–2))	0.023098(0.04502)[0.51311]	–0.014959(0.13562)[–0.11030]	–0.040699(0.12695)[–0.32060]	0.038289(0.02152)[1.77934]
Δ(CREDIT(–1))	–0.449378(0.24723)[–1.81763]	–0.523686(0.74482)[–0.70310]	–2.006198(0.69721)[–2.87746]	–0.164163(0.11818)[–1.38904]
Δ(CREDIT(–2))	–0.228187(0.25115)[–0.90856]	–1.955085(0.75663)[–2.58392]	–0.717031(0.70827)[–1.01237]	–0.165490(0.12006)[–1.37842]
C	0.141393(0.30740)[0.45997]	0.052112(0.92608)[0.05627]	2.364416(0.86688)[2.72751]	0.314642(0.14694)[2.14124]
R-squared	0.352079	0.519839	0.602038	0.153108
Adj. R-squared	0.263323	0.454064	0.547523	0.037096
Sum sq. resids	396.5127	3598.770	3153.376	90.60734
S.E. equation	2.330597	7.021269	6.572437	1.114090
F-statistic	3.966808	7.903236	11.04347	1.319755
Log likelihood	–184.3703	–277.0071	–271.4581	–122.3710
Akaike AIC	4.651673	6.857312	6.725194	3.175500
Schwarz SC	4.969994	7.175634	7.043515	3.493822
Mean dependent	0.078410	0.687817	3.616846	0.345238
S.D. dependent	2.715367	9.502646	9.770754	1.135347

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

The VECM estimates can be summarized using the following equation

Δ(REER) = – 0.393169*(REER(–1) + 0.136724740451*RESERVES(–1) – 1.57683971132*CREDIT(–1) – 63.1479402277) + 0.090454*(OIL(–1) + 0.354604988832*RESERVES(–1) – 6.3657188568*CREDIT(–1) + 127.845523865) + 0.288864*Δ(REER(–1)) + 0.407714*Δ(REER(–2)) – 0.091516*Δ(OIL(–2))

From the aforementioned equation, we find that commodity price shocks do not significantly explain the impact on exchange rate adjustment except for the level of commodity prices (Table 16). This could possibly be due to the fact that even though India imports large amount of commodities, it also exports significant amount of commodities after value-adding. Financial markets (credit to private sector) and level of foreign exchange reserves moderate the impact on the exchange rate adjustment. The impulse response functions (Figure 13) indicate that the impact lasts up to eight quarters after which the effect tends to become constant.

Table 13.

Unrestricted Cointegration Rank Test (Trace)

HypothesizedNo. of CE(s)	Eigenvalue	Trace	0.05	Prob.**
HypothesizedNo. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**
None *	0.296361	52.18204	47.85613	0.0186
At most 1	0.163251	23.00834	29.79707	0.2457
At most 2	0.093976	8.215129	15.49471	0.4427
At most 3	0.000288	0.023923	3.841466	0.8770

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

Notes: Trace test indicates 1 cointegrating eqn(s) at the 0.05 level.

* denotes rejection of the hypothesis at the 0.05 level.

**MacKinnon–Haug–Michelis (1999) p-values.

CE: cointegrating equations.

Table 14.

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

HypothesizedNo. of CE(s)	Eigenvalue	Max-Eigen	0.05	Prob.**
HypothesizedNo. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**
None *	0.296361	29.17370	27.58434	0.0310
At most 1	0.163251	14.79321	21.13162	0.3036
At most 2	0.093976	8.191206	14.26460	0.3596
At most 3	0.000288	0.023923	3.841466	0.8770

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

Notes: Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level.

* denotes rejection of the hypothesis at the 0.05 level.

**MacKinnon–Haug–Michelis (1999) p-values.

CE: cointegrating equations.

Table 15.

Cointegration of REER, Commodity Prices, Foreign Exchange Reserves and Credit

Cointegrating Equation	Cointegrating Equation 1
REER(–1)	1.000000
CPI(–1)	–0.302502(0.06358)[–4.75771]
RESERVES(–1)	0.136270(0.03839)[3.55004]
CREDIT(–1)	–0.943357(0.30485)[–3.09448]
C	–64.36285

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

Table 16.

VECM of REER, Commodity Prices, Foreign Exchange Reserves and Credit

Error Correction	Δ(REER)	Δ(CPI)	Δ(RESERVES)	Δ(CREDIT)
CointEq1	–0.570453(0.11182)[–5.10135]	–0.320025(0.21672)[–1.47668]	–0.627836(0.36188)[–1.73491]	–0.011077(0.05739)[–0.19303]
Δ(REER(–1))	0.239055(0.10683)[2.23775]	–0.219944(0.20704)[–1.06234]	0.603284(0.34572)[1.74501]	–0.010653(0.05482)[–0.19433]
Δ(REER(–2))	0.443106(0.11589)[3.82348]	0.599168(0.22460)[2.66770]	0.925846(0.37505)[2.46862]	–0.036762(0.05947)[–0.61813]
Δ(REER(–3))	0.078949(0.12836)[0.61508]	0.632835(0.24876)[2.54399]	0.437917(0.41538)[1.05425]	0.005980(0.06587)[0.09078]
Δ(CPI(–1))	–0.008323(0.07564)[–0.11004]	0.200532(0.14658)[1.36803]	–0.227241(0.24477)[–0.92838]	0.034073(0.03881)[0.87784]
Δ(CPI(–2))	–0.004868(0.07593)[–0.06411]	–0.203333(0.14716)[–1.38171]	–0.310483(0.24573)[–1.26349]	0.019831(0.03897)[0.50893]
Δ(CPI(–3))	–0.003604(0.07757)[–0.04646]	–0.140743(0.15033)[–0.93624]	–0.224212(0.25102)[–0.89319]	–0.020466(0.03981)[–0.51415]
Δ(RESERVES(–1))	0.048216(0.04431)[1.08824]	0.245075(0.08587)[2.85409]	0.743733(0.14339)[5.18694]	–0.007924(0.02274)[–0.34851]
Δ(RESERVES(–2))	0.044278(0.05254)[0.84276]	–0.063324(0.10182)[–0.62190]	–0.097884(0.17003)[–0.57569]	0.017290(0.02696)[0.64129]
Δ(RESERVES(–3))	–0.018675(0.05009)[–0.37285]	–0.050807(0.09707)[–0.52341]	–0.038793(0.16209)[–0.23933]	0.026212(0.02570)[1.01978]
Δ(CREDIT(–1))	–0.616131(0.25342)[–2.43128]	–0.225965(0.49113)[–0.46009]	–1.025238(0.82011)[–1.25012]	–0.218579(0.13005)[–1.68076]
Δ(CREDIT(–2))	–0.392408(0.24982)[–1.57078]	–0.427635(0.48416)[–0.88326]	–0.092932(0.80846)[–0.11495]	–0.174625(0.12820)[–1.36212]
Δ(CREDIT(–3))	–0.234680(0.23922)[–0.98101]	0.992454(0.46362)[2.14064]	1.546697(0.77418)[1.99786]	–0.113773(0.12276)[–0.92677]
C	0.194573(0.31812)[0.61163]	–0.027620(0.61654)[–0.04480]	1.591974(1.02952)[1.54633]	0.384957(0.16325)[2.35802]
R-squared	0.445304	0.506747	0.551783	0.164512
Adj. R-squared	0.340797	0.413815	0.467337	0.007101
Sum sq. resids	339.0410	1273.437	3550.787	89.28647
S.E. equation	2.216673	4.295999	7.173610	1.137544
F-statistic	4.260967	5.452897	6.534104	1.045114
Log likelihood	–176.1740	–231.0932	–273.6494	–120.8018
Akaike AIC	4.582507	5.905861	6.931310	3.248236
Schwarz SC	4.990504	6.313858	7.339308	3.656233
Mean dependent	0.067989	0.496705	3.600853	0.349398
S.D. dependent	2.730184	5.611087	9.829045	1.141605

Source: World Bank Economic Database, Ministry of Statistic and Programme Implementation, Ministry of Finance, GoI; Federal Reserve Bank of St. Louis and RBI (2005).

The VECM estimates can be summarized using the following equation

Δ(REER) = – 0.570453*(REER(–1) – 0.302502365474*CPI(–1) + 0.13626977089 *RESERVES(–1) – 0.94335731799*CREDIT(–1) – 64.3628532796) + 0.239055*Δ(REER(–1)) + 0.443106*Δ(REER(–2)) – 0.616131*Δ(CREDIT(–1))

Summary and Conclusion

We find empirical evidence on the impact of global commodity and oil price fluctuations on domestic macroeconomic performance of India. Non-energy commodity and oil price fluctuations significantly explain the variation in output and prices in India. While output is negatively affected, commodity and oil price spikes are inflationary in their impact. Moreover, impact of commodity and oil price shocks on inflation is faster than on output with expected characteristics of negative supply shock driven cost-push inflation. It is discernible to note that the impact of commodity and oil price spikes last for three quarters whereas their impact on output is more lingering for in that they last for eight quarters or more.

The RER adjusts significantly to oil price shocks for that RER depreciates just about with two quarter lags but having more lingering effect for in that it lasts for eight quarters. The levels of foreign exchange reserves and credit to private sector (financial markets) moderate the commodity and oil price shocks. Since RER adjustment takes place after two-quarter lags, it provides monetary authority (central bank) a time window to intervene and contain the volatility through intervention. However, as observed, change in reserve variations are not found significant in explaining the exchange rate adjustment. It is not surprising since monetary authority (RBI) in India did not try to target the exchange rate per se, but only contained the volatility in its extreme. In fact, either monetary authority is following ‘leaning against the wind’, that is, allowing nominal depreciation in the wake of capital flows or in the post-2009–2010, or, of late, monetary authority almost followed a hands-off approach in letting exchange rate be determined by macroeconomic fundamentals.

However, given that monetary authority is moving towards inflationary-targeting framework, it may insulate the inflationary impact of commodity and oil price shocks either by stabilizing the exchange rate through active intervention, followed by sterilization or by using interest rate to contain inflationary impact borne out of these shocks. Further, monetary policy actions may be complemented by countercyclical fiscal policy given that commodity and oil price shocks are significant in their impact on output variation.

Footnotes

Acknowledgements

The article is an outcome of the completed Seed Money Project SM 230 and earlier versions of the article has been presented at the 8th International Research Conference 2015, organized by Central Bank of Sri Lanka in Colombo, 5 November 2015 and at Twenty Fourth Annual World Business Congress, organized by International Management Development Association held in Famagusta, North Cyprus, 27–31 May 2015.

pplying usual disclaimer, we thank anonymous referee(s) of the journal as well as participants of the conferences for their useful suggestions.

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