1 Introduction

I

Kaldor (1976) argued that any substantial increase in commodity prices will have a powerful inflationary effect on industrial costs and prices. He further argued that any large change in commodity prices will have a dampening effect on industrial activity, in addition to indirect price and wage effects. The traditional view for relying on commodity prices in predicting the general price level is that these prices usually enter with lags in the costs of output prices. Labys and Maizels (1993) by employing Granger causality (hereafter GC) tests between commodity prices and macroeconomic variables for major countries of the Organisation for Economic Cooperation and Development (OECD) conclude that the highest degree of causality exists between international primary commodity prices and selected macroeconomic indicators including national prices and industrial production. Labys and Maizels (1993) showed that commodity prices led changes in interest rates and, to a lesser extent, adjustments in the money supply, suggesting that the major countries did, in fact, practise monetary policy adjustments of some form, to counter major commodity price swings.

august 20, 2011 vol xlvi no 34

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Very recently, in the monetary policy statement for 2011-12, the Reserve Bank of India (RBI) had also shown serious concern regarding inflationary risks emanating from rising commodity prices and its downside risks to growth. Considering the domestic demand-supply balance and the global trends in commodity prices, the RBI had revised the baseline projection for wholesale price index (WPI) inflation for March 2012 at 6% with an upward bias. RBI began exiting from the crisis-driven accommodative policy in October 2009 and since then had kept revising the policy rates upwards. However, in view of the overall inflation scenario, the RBI announced a further upward revision in the policy rates in its monetary policy statement for 2011-12.

The RBI concern over rising international commodity prices makes it imperative to look for how international commodity prices affect India’s domestic price level. Rakshit (2011) had discussed the linkages between selected macroeconomic indicators including domestic sectoral and overall price indices in the Indian context in the time-domain framework. However, it is reasonable to believe that domestic sectoral price indices act as a lagging indicator and international commodity prices as a leading one (mainly in the case of industrial inputs). Moreover, the strength of “pass-through” of international commodities prices in the domestic price level and industrial activity may vary over different time horizons. In other words, the strength of causality running from international commodities prices into domestic price level and output may vary in the short run, over the business cycle frequencies and in the long run. Therefore, the usual time domain representation may not necessarily be the most revealing, in cases such as business cycle analysis, because important economic information may be hidden in the frequency content of the time series. In order to access this information we need to transform a time series from the time domain and represent it in another domain. Granger (1969) had suggested that a spectral-density approach of GC would give a richer and more comprehensive picture than a one-shot GC measure that is supposed to apply across all periodicities.

In what follows, this essay aims to assess the “pass-through” of international commodities prices to manufacturing products prices and its effect on manufacturing sector output. We also analyse the IMF’s overheating viewpoint in the Indian context. We apply the GC test in the frequency domain proposed by Lemmens et al (2008) to see if the observed causality correspond to the business cycle frequencies or from too high (short run) or too low (long run) frequencies (see Appendix, p 50).1

The plan of the paper is as follows. Section 2 describes the data and Section 3 discusses the causality test methodology. Section 4 provides the empirical finding and Section 5 concludes the paper.

2 The Data

For this study we have used monthly data for the period 1994:4 (fourth quarter of 1994) to 2010:12 (twelfth quarter of 2010). We have considered India’s Index of Industrial Production (IIP) for the manufacturing sector at the base year 1993-94 as a measure of output. WPI of manufacturing prices has been considered as a proxy for prices. The base years of the WPI series are 1993-94 and

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2004-05. We have extrapolated the new series for the period 1994:4 to 2004:3 by using the growth rate of the previous series with base year 1993-94. Data for IIP was collected from the Central Statistical Organisation (CSO – http://mospi.nic.in/cso_test1.htm) and data for the WPI from the Office of the Economic Adviser (Government of India). Monthly (non-seasonally-adjusted) data for the Industrial Materials Index (IMI) and Energy Index (EI) at base year 2005 had been taken from the IMF’s primary commodity prices database (http://www.imf.org/external/np/res/commod/index.asp). In the IMI, agriculture raw materials have a nearly 42% weight and metals has 58%. In the EI, spot crude p etroleum has an 85% weight, natural gas has an 11% weight and coal has a 4% weight.

Figure 1 present the year-on-year (Y-O-Y) growth rate of the monthly IMI and EI and Figure 2 presents the Y-O-Y growth rate of India’s monthly WPI for the manufacturing sector for the period 2008:1 to 2010:12 which broadly incorporates the recent crises period. Figure 1 suggests that the growth rate of IMI and EI started falling from the middle of 2008 and then again shown an uptrend from the mid of the year 2009.

Figure 1: Year-on-Year Growth Rate of IMI and EI with Base Year 2005

Y-O-Y percentage growth

100 80 60 40 20 0 -20 -40 -60 2008-01 2008-05 2008-09 2009-01 2009-05 2009-09 2010-01 2010-05 2010-09 2010-11 EI IMI

Source: IMF’s primary commodity prices database (http://www.imf.org/external/np/res/ commod/index.asp).

A similar trend was observed in India’s WPI for the manufacturing sector Y-O-Y growth rate (Figure 2). The WPI for the manufacturing sector Y-O-Y growth rate started falling after reaching 8.55% in August 2008 and remained negative for three consecutive months, 2009:6-2009:8. However, Y-O-Y growth of WPI for the manufacturing sector witnessed a reversal in its movement in September 2009. Prima facie, Y-O-Y growth rate movement in IMI and EI and somewhat a similar trend in India’s WPI for manufacturing provide evidence in support of the transmission of international commodities prices to the domestic price level.

Figure 2: Year-on-Year Growth Rate of India’s WPI for Manufacturing Sector at Base Year 2004-05

Y-O-Y percentage growth 10

8 6 4 2 0 2008-01 2008-05 2008-09 2009-01 2009-05 2009-09 2010-01 2010-05 2010-09 2010-11 WPI

-2

Source: Office of the Economic Adviser (http://eaindustry.nic.in/#), Government of India.

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Granger coefficient of coherence

be necessary to use seasonal rather than annual differencing to attain stationarity. Therefore, we have applied the Beaulieu and Miron methodology for testing unit roots in monthly data (Beaulieu and Miron 1993). They used the approach developed by Hylleberg et al (1990) to derive the mechanics of another pro

0.4

0.2

cedure for testing seasonal unit roots using monthly data. All the four variables, IIP, WPI, IMI and EI have been logarithmically transformed. The unit roots test results are presented

0.3 0.6 0.9 1.2 1.5 1.8 2.0 2.3 2.6 2.9 3.0

““

Table 1: Beaulieu and Miron Test for Integration at Seasonal and Non-Seasonal Frequencies

In Figure 4 we have presented the result of the Granger causal

ʌ ʌ

ʌ ± ʌ

0

± ʌ ± ʌ

Granger coefficient ofcoherence

stationary in levels. Therefore we cannot reject the null hypothesis, i e, the presence of unit roots at seasonal frequencies as well as at zero frequency in all the four time series, IIP, WPI, IMI and EI. Following from the above result, all the four variables were season

0.2

ally differenced and further first differenced, which leaves us

0

0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.0 2.3 2.6 2.9 3.0

with 188 observations. The differenced series ¨ 12¨of IIP, WPI, IMI

and EI were found stationary.

Frequency

As in Figure 3.

In the parenthesis, associated P-value has been given. The (*) reflects the fact that we cannot reject the null hypothesis of presence of unit roots at 5% level of significance. The test has been performed using the software package “uroot” in ‘R’.

The results in Table 1 show that all the variables were nonity running from EI to WPI. At 5% level of signficance, EI Granger causes WPI at frequencies corresponding to cycles longer than six months. Therfore, EI Granger causes WPI at frequencies corresponding to short as well as long run business cycle frequencies. The Granger coefficient of coherence reaches its peak, i e, 0.37 at frequencies corresponding to long cycles, which highlights that the causality running from EI to domestic prices becomes much stronger at longer cycles.

Figure 4: Causality from International Energy Index to WPI Manufacturing

0.4

To apply the above mentioned GC test methodology, all the four differenced series (i e, ¨ 12¨of the series) of IIP, WPI, IMI and EI have been filtered using ARMA models to obtain the innovation series. After ARMA filtering the series and adjusting for lags, we are left with 176 observations. Therefore, we can consider N = 88 cycles of different frequencies, with the shortest possible cycle of two months and longest cycle of 176 months. We have used lag length M = ¥7 . The frequency (Ȝon the horizontal axis can be translated into a cycle or periodicity of T months by T = 2ʌȜ where T is the period.

The results presented in Figures 3 and 4 suggest that both IMI and EI Granger causes WPI in frequencies corresponding to short as well as long run business cycle frequencies. However, the causlity running from IMI to WPI is relatively stronger compared to that of from EI.

We now look for evidence for causality running from IMI and EI to IIP. In Figure 5 (p 49) we have presented the result of Granger causality running from IMI to IIP. The result indicates that at 5% level of significance IMI Granger causes IIP at frequencies corresponding to 12-58 month cycles. This represents that

Analysing time series in the frequency-domain, i e, spectral analysis, could be helpful in supplementing the information obtained by time domain analysis (Granger 1969 and Priestley 1981). Spectral analysis highlights the cyclical properties of data. In particular, it allows us to decompose the variance of a stationary time series by variance attributed to cycles of various frequency. Similarly, the co-movement in a pair of stationary time series can be decomposed by cycles of various periodicities. This study uses the methodology proposed by Lemmens et al (2008) to decompose causality between pairs of stationary times series at cycles of different periodicities. The details of the methodology are given in Appendix 1.

3 Empirical Findings

We first test for stationarity of all the four series. Franses (1991) suggested that if the stochastic process is non-stationary, it is important that we investigate the presence of unit roots at all frequencies, not just the long run, because in this case it would