This paper aims to find the pass-through of oil price to consumer and producer prices in Pakistan. We estimate a recursive VAR model to investigate pass-through of oil prices to domestic prices for the period of July 1991 to December 2015. The findings of the paper are (1) the oil price have moderate effect on domestic inflation (2) oil price pass-through is stronger in WPI than CPI (3) the impact of oil price pass-through is more pronounced in the period 2008 to 2015 (4) oil price has asymmetric impact on domestic inflation.

Keywords: Oil Price Pass-through, CPI, WPI, Recursive VAR, Pakistan.

1. INTRODUCTION

The oil prices gained the attention of macroeconomists in the last three decades. The relationship between oil prices and inflation is very important for monetary policy and firms' pricing decisions. Oil prices and overall inflation are positively related because production and transportation cost includes oil as major input. There are two major groups, transportation and power generation, which consume oil 47 percent and 43 percent, respectively. In Pakistan, 62.4 percent electricity is produced through thermal sources [Economic Survey of Pakistan (2013-14)].

Oil prices have asymmetric impact on oil importing and exporting countries. [Naurin and Qayyum (2016)]. After 1970's, the relationship between oil prices and inflation became famous. This relationship deteriorated after 1980 for developed countries but relationship still exists for small oil importing countries like Pakistan. [Barsky and Kilian (2002)].

Pakistan is small open economy in which 32 percent of the energy demand is fulfilled by oil and 82 percent of the oil demand is fulfilled by importing from international market. [Malik (2008)]. The shock in oil prices in international market in 2008 had a big effect on Pakistan economy.

The performance of macroeconomic variables is affected by oil prices which have six channels such as supply side shock effect, wealth transfer effect, inflation effect, real balance effect, sector adjustment effect, and unexpected effect [Brown and Yucel (2002); Jones, et al. (2004); Tang, et al. (2010); Khan and Ahmed (2011)]. According to supply side shock channels, as oil prices increase, output decreases, unemployment increases and income decreases. In the wealth transfer channel, the effect of oil price increase transfer the wealth from oil importing country to oil exporting country. The third channel, CPI basket has major components of oil based products. So increase in oil prices leads to increase in inflation rate. In fourth channel, increase in oil prices would lead to increase in money demand. In this regard, monetary authority tightens monetary policy by increasing the interest rate; discourage investment and decreases output in the long run. The fifth channel is asymmetric impact of oil prices within the sectors of the economy. In fifth channel the asymmetric impact of oil prices depends on monetary policy, adjustment cost and effects of uncertainty on investment. In the last channel, uncertainty about duration of oil price shocks damage the economy.

In Pakistan, there are nine oil marketing companies (OMCs). The state owned company PSO is oil market Leader Company beside Caltex and Shell etc. [Malik (2008)]. The government kept tight control over the petroleum product prices. The pricing decisions were made on political basis. In 1999 the government transferred oil pricing decisions to Oil and Gas Regulatory Authority (OGRA).

In developing countries like Pakistan the governments for political reasons subsidises fuel prices. So the consumers do not face international oil prices. Producer will shift the higher input cost to consumer after time lag [Jongwnich (2011); Khan and Ahmed (2011)].

Our aim is to investigate oil price pass-through to consumer and producer inflation for the monthly data from 1991-8 to 2015-12. There is no study available for Pakistan on this topic but few studies have been done on oil prices, growth and inflation. Unlike past studies on the topic, this study is pioneer for oil prices pass-through. We build our model on McCarthy (2000) but we have made changes in recursive structure and estimated as VAR model for both CPI and WPI inflation. Moreover, we also find the asymmetric effect of oil prices on both CPI and WPI inflation which would be useful information for policy-maker.

The rest of the study is organised as; Section 2 reviews literature. Section 3 explains the model and the data. Section 4 provides results and discussion. Section 5 describes conclusion and policy implications.

2. LITERATURE REVIEW

In this chapter we review literature on effect of oil prices on inflation. Mostly literature is available for developed countries and majority of literature found that impact of oil prices on inflation reduced over time. The Global event and external shocks affect the inflation rate with varying pass-through rates from country to country due to integration of the economies. Oil prices got the attention after the 1973-74 and neoclassical believe that it is supply side shocks which increase general prices [Abel and Bernanke (2005)].

Bhattacharya and Bhattacharyya (2001) concluded that oil prices took five to seven months to pass its shocks to inflation in India. Hooker (2002) found oil prices pass-through had been negligible since 1980. There was no effect of oil shocks on inflation [Atukerens (2003)].

De Gregorio, et al. (2007) analysed the panel data of 34 developing and developed countries by using Phillips curve and rolling VAR and found general oil prices pass-through was declining over the time. He explained the reasons why oil prices pass-through was low: developed countries are low oil intensity economies, low exchange rate pass-through ; therefore, firms change prices less frequently. Blanchard and Gali (2007) explained the reasons of low oil pass-through, such as monetary policy, wage flexibility and structural changes in industries.

Duma (2008) analysed effect of oil prices on food prices and import prices by using VAR for Sri Lanka. The results showed incomplete pass-through effect. Alvarez, et at. (2009) assessed Spain and Euro economies for oil prices pass-through. The result showed that oil prices had less pass-through effect to domestic inflation. Chen (2009) carried out the study for 19 industrialised countries and results showed oil prices pass-through varies across the countries but it is recently declining. Lombardi (2009) used the Global VAR and found that pass-through is less in emerging economies.

Kiptui (2009) analysed oil prices and exchange rate of Kenya's economy. The results confirmed the incomplete pass-through because inflation was affected by domestic demand. Shioji and Unchino (2010) examined Japan economy for oil prices pass-through by using the VAR and found that oil prices pass-through had decreased and economy depend less on oil intensive production system.

Chou and Tseng (2011) examined the oil prices pass-through for Taiwan economy using ARDL model with Augmented Phillips curve, rolling and recursive regression. The results showed oil prices pass-through is present in long run and absent in short run. Adenuga, et al. (2012) used the ARDL to investigate the oil prices pass-through and found it incomplete.

Catik and Karacuka (2012) used Markov switching (MS-VAR) model and found incomplete pass-through. Gao, et al. (2013) used bivariate VAR to assess the pass-through to disaggregate CPI groups. They found the asymmetric effect. Niyimbanira (2013) used different technique and applied Cointegration method. And found unidirectional relationship between oil prices and inflation. Same result was found by Sukati (2013) and Wong (2013).

Valcarcel and Wohar (2013) estimated a Bayesian structural VAR for USA economy and found negligible oil prices pass-through. Blinder and Rudd (2008) explained three causes of low oil prices pass-through which were explained by Blanchard and Gali (2007). Bemake, et al. (1997) showed tight monetary policy was one of reason of low oil prices pass-through.

Dedeoglu and Kaya (2014) found increasing trend of oil prices pass-through but other researcher found decreasing trend in Turkey. They explained reason of increasing trend of oil prices pass-through because oil became important factor in cost structure. They also found that oil prices had more impact on PPI than on CPI.

Limited number of studies is available on oil prices shocks in case of Pakistan. Khan and Ahmed (2011) examined how external shocks affect the domestic inflation by using SVAR model. They found that oil prices affect inflation positively. Jafri, et al. (2012) found same results using OLS. Subhani, et al. (2012) found the unidirectional causality between the crude oil prices and inflation. Ansar and Asghar (2013) found positive relationship between oil prices, stock prices and inflation. Same pattern of analysis was used by Khan, et al. (2015) who found causality between CPI and crude oil prices and exchange rate. Saleem and Ahmad (2015) analyse that oil prices had positive impact on money supply, crude oil prices, exchange rate, interest rate and indirect taxes, while real GDP has negative impact on inflation.

Naurin and Qayyum (2016) examined the oil prices effect on domestic inflation using financial time series econometrics technique. They concluded that oil prices and inflation had positive relationship but positive news had more effect than negative news.

The above studies have employed different methodologies to estimate the effect of oil prices to the inflation. They found different results for different economies and different rate of pass-through . Though in developed countries the pass-through rate has decreased over the time, but in small open and oil importing economies like Pakistan the issue still exist. In order to fill the gap, this research is initiated. The primary focus of this research is to workout oil prices pass-through for CPI and WPI.

3. MODEL AND DATA

To assess the pass-through of Oil prices to Consumer price Inflation (CPI) and Wholesale Price Inflation we used recursive Vector Autoregressive (VAR) approach proposed by McCarthy (2000). The model is based on six variables; Consumer Price Inflation [[product].sup.CPI.sub.t], Wholesale Price Inflation [[product].sup.WPI.sub.t], the growth of Money Supply [DELTA][M2.sub.t], Nominal bilateral Exchange Rate [DELTA][e.sub.t], Demand Shock (proxy by Quantum Index of Manufacturing (QIM)) [DELTA][y.sub.t] and Supply Shock (proxy by Oil Price Inflation) [[product].sup.oil.sub.t .

The model is restricted in recursive pattern as:

[mathematical expression not reproducible]

In this model [E.sub.t-1] (.) refers to expectation of variable based on the information available at the end of previous period t - 1.

The methodology of the study depends on a model of pricing along distribution chain. Inflation at a particular distribution stage CP1 or WPI in period t is assumed to be comprised of different components. The first component is the expected inflation. The second and third components are supply and demand shocks. The fourth component we include is the growth of money supply and exchange rate shocks on inflation followed by effect of inflation shocks at previous stage of distribution chain.

We have imposed recursive structure on our VAR model. Oil prices are assumed to be exogenous because no other variable contemporaneously affect oil prices. This assumption is reasonable as Pakistan is an oil importing country and its share in world market is very small. Changes in oil prices affect economic activity as oil is input m production (specifically industrial production which we have taken as measure of economic activity). Money supply is adjusted with changing economic activity. Moreover, changes in import bill due to changes in oil prices affect net foreign assets which are component of money supply. A change in net foreign assets affects foreign exchange reserves and has implications for exchange rate movements. In later stage, exchange rate, money supply, aggregate economic activity affect general price level. This effect is first reflected in wholesale market (WPI) and then the changes in prices are passed on to consumers in final stage.

McCarthy (2000) developed this model to assess the exchange rate pass-through . The same model is used by Dedeoglu and Kaya (2014) to examine the oil prices pass-through but they did not include the monetary policy reaction function for Turkey. In our study we include monetary reaction function in line with the model of McCarthy (2000).

We recover structural shocks by Cholesky Decomposition of the residual variance covariance matrix. There are two methodologies used to assess the oil prices pass-through; first impulse response of WPI and CPI inflation and cumulative pass-through coefficients. Second, we find variance decomposition of WPI and CPI inflation, which are used to assess how much of the forecast error variance of domestic price indices is explained by oil prices over this time period.

For analysis from VAR the ordering of the variables is crucial. The following ordering for the impulse response analysis is used.

[[product].sup.oil.sub.t] [right arrow] [DELTA]Y [right arrow] [DELTA]M2 [right arrow] [[product].sup.WPI.sub.t] [right arrow] [[product].sup.CPI.sub.t]

McCarthy (2000) ranked central bank monetary reaction function at last. But in this study we follow Hyder and Shah (2004) ordering.

The effects of an increase and decrease in oil prices on the CPI and WPI inflation are found to be different. Rodriuez and Sanchez (2004) investigate the nonlinear impact of oil prices on GDP. We also consider his asymmetric specification, in which increase and decrease in the oil prices are treated as separate variables following the methodology of Rodriuez and Sanchez (2004). The asymmetric specification distinguishes between positive and negative changes in oil prices, which are defined as follow:

[O.sup.+] = [O.sub.t] {if current oil price is greater than that in the previous time period} = 0 {otherwise}

[O.sup.-] = [O.sub.t] {if current oil price is less than that in the previous time period} = 0 {otherwise}

After getting positive and negative values than we filter these variables and assess the positive and negative effect of oil prices on inflation.

We used monthly data in study ranging from July 1991 to December 2015. The source of data for all variables except oil prices is taken from SBP Statistical Bulletin, while international oil prices are taken from International Financial Statistics (IFS) CD and converted in domestic prices by multiplying with exchange rate.

4. RESULTS AND DISCUSSION

Before we proceed to estimate recursive VAR, first we seasonally adjust all six variables using the X13 method. We make two sub-samples of the data, from 1991-8 to 2007-12 (increasing trend) and 2008-01 to 2015-12 (high rate of fluctuation). We make sub samples on the basis of oil prices trend (see Figure 1). Hyder and Shah (2004) also make sub samples for exchange rate pass-through .

Before starting estimation we will first establish order of integration of the series and optimal lag length. The ADF test is used to test stationarity which suggest that all variables are integrated of order one. The resuhs of Augmented Dickey Fuller test are presented in Table 1. We estimate the recursive VAR at first difference with two lags as optimal lag length.

The result of impulse response function of CPI and WPI are described in Figure 2 from which oil prices pass-through to domestic inflation may be seen.

Oil price shocks have positive effect on domestic price level. The Figure 2 displays IRF of CPI and WPI to a positive one standard deviation shock to oil prices So shocks in oil prices affect the CPI and WPI. CPI increase first and decrease till four month and then increase till six month than decreases and reaches zero. The WPI start decreasing but reaches zero in twelve months. As for as sub sample analysis of CPI and WPI are concerned, CPI responds more in the period of 2008-1 to 2015-12 and WPI also responded more in the last period. (See Appendix Figures 3 and 4). So oil price shocks cause inflationary pressure on the Pakistan's economy. Khan and Ahmed (2011) and Dedeoglu and Kaya (2014) obtain the similar results.

The effect of oil price shock is stronger in the case of WPI relative to CPI The pass-through coefficients indicates that after ten months, 0.933919 the oil prices change has already been reflected into wholesale prices. In the consumer prices this change will continue and once shock come its effect remains till twelve months. The WPI respond to shocks more because: (1) larger share of tradable commodities in WPI (2) CPI include services and therefore is less affected by oil prices (3) the oil prices have pronounced effect in WPI because of the cost of production.

We calculate cumulative pass-through coefficient for sub-samples which indicate that the oil prices pass-through to consumer prices and producer prices has increased 0.066825 to 0.840808 and 0.299208 to 1.858539 (see Table 2). And sub samples results confirm that oil prices have high pass-through effect on domestic prices.

Results of varance decomposition are given in Table 3, which show the contribution of innovation in the oil prices to the variability of WPI, CPI and other variables.

As shown in Table 3, the oil price shocks only explain 4.43 and 17.51 of the forecast error variance of CPI and WPI respectively, while the remainder of the variance of CPI and WPI are explained by innovations in other variables. Specifically, 66.30 of the variance is explained by its own innovation followed by industrial output growth which explains 0.85 of variance. And 1.85 of the variance is explained by growth of money supply innovation followed by exchange rate which explains 4.03 of variance. In the case of WPI, 76.72 of variance is explained by its own impact, 17.52 of variation is explained by oil prices followed by money supply growth (1.23). Shah and Wang (2012) obtain the similar results for Pakistan.

The variance decomposition of sub-samples shows that the explanation of forecast error variance due to oil prices increased from 2008-01to 2015-12 in case of CPI and WPI (see Table 4 and Table 5 in Appendix). Three sub-samples are investigated in case of WPI and results show that in last sub-sample oil prices have greater impact on overall price level.

Moreover, the variance decomposition analysis shows that 2008-01to 2015-12 is more susceptible to oil prices. The estimated cumulative pass-through coefficients in sub-sample 2008-01to 2015-12 (0.84) is reflecting high rate of oil prices pass-through to WPI. So oil prices explain more variation in the WPI than CPI.

The results provide us evidence against the linear approach that assumes that oil prices have symmetric effect (see Figures 5 and 6 and Table 6 and Table 7 in Appendix). Oil prices have asymmetric effect on domestic price level. The Figures 5 and 6 in Appendix display IRF of CPI and WPI to a positive one unit standard deviation shocks to oil prices. So increase in oil prices affects the CPI more than the decrease of oil prices. The estimated cumulative pass-through coefficient is 0.75 when oil prices increase and 0.27 when oil prices decrease. The variance decomposition analysis shows 13.65 and 11.10 for oil prices increase and decrease effects. So it shows that oil price increase has more effect on CPI inflation than oil price decrease does. Rodriuez and Sanchez (2004) obtained similar results for CPI inflation.

The estimated cumulative pass-through coefficients of WPI is found 1.13 for oil prices increase and -0.02 for oil price decrease. The variance decomposition analysis shows that 14.28 and 11.43 of WPI inflation is explained by oil prices increase and decrease, respectively. So WPI responds more to the oil price increase than oil prices decrease'. Our result conform that oil prices have asymmetric effect and oil prices increases affect more domestic prices than oil prices decrease.

5. CONCLUSION AND POLICY RECOMMENDATION

In this paper, we use recursive VAR model suggested by McCarthy (2000) on monthly data from July 1991 to December 2015 and made three sub-samples. The findings of the paper are (1) the oil prices have a moderate effect on domestic price inflation (2) oil prices pass-through is more pronounced in WPI as compared to CPI due to the higher share of tradable in WPI basket relative to CPI basket. The gap between these two pass-through rates depend on the ability of producer to passhigher costs on the consumers [Dedeoglu and Kaya (2014)]. When oil prices become important input, producers respond more to oil prices [Shioji and Unchino (2010)]. (3) The impact of oil prices pass-through on domestic prices spreads over twelve months. However the effect is more pronounced in first six months. (4) The impact of oil prices pass-through is more pronounced in period of 2008 to 2015. (5) Oil prices have asymmetric impact on the domestic inflation. Oil price increase has more effect than oil price decrease on CPI while in the case of WPI the oil prices increase has more impact than oil prices decrease, because producers very quickly respond to the increase in oil prices.

In response to oil price shock, SBP can react to inflation rate with asymmetric response coefficients (in policy reaction function) depending on the increase or decrease in inflation rate. However, this should be done with caution as tight monetary policy during high inflationary period may render high sacrifice ratio.

APPENDIX

Table 4
Variance Decomposition of CPI Prices

Aug 1991 to Dec 2007

Forecast Horizon     OIL         MP         GM2

1                  0.967531   0.008492    0.117847
2                  1.166768   0.111006    0.227117
3                  1.122334   1.115490    0.688404
4                  1.630127   1.076342    2.898904
5                  1.661102   1.108565    3.158105
6                  1.680566   1.107347    3.179904
7                  1.674679   1.103063    3.456726
8                  1.672683   1.101966    3.523979
9                  1.671122   1.101053    3.603084
10                 1.671368   1.100669    3.665406
11                 1.670958   1.100857    3.694196
12                 1.670613   1.101575    3.715272

Jan 2008 to Dec 2015

Forecast Horizon     OIL         MP         GM2

1                  5.905906   0.076429    1.915641
2                  10.85528   0.198598    3.742074
3                  15.09552   3.980790    3.165330
4                  15.67023   5.170055    2.966421
5                  14.12669   4.675270    4.335065
6                  14.28037   4.598030    4.175450
7                  14.04139   4.693786    4.110558
8                  14.14722   4.665992    4.093130
9                  13.91159   4.608495    4.164384
10                 14.91455   4.636626    4.146952
11                 14.05877   4.6211836   4.139743
12                 14.05448   4.660751    4.135131

Aug 1991 to Dec 2007

Forecast Horizon      ER        WPI        CPI

1                  0.121666   13.24350   85.54096
2                  0.336559   13.13440   85.02415
3                  1.442982   14.41609   81.21470
4                  1.423443   13.65219   79.31899
5                  1.529694   13.83463   78.70790
6                  1.693743   13.79842   78.54003
7                  1.709124   13.73700   78.31941
8                  1.744216   13.73400   78.22316
9                  1.743046   13.72076   78.16093
10                 1.745695   13.70930   78.10756
11                 1.753673   13.70365   78.07666
12                 1.754740   13.70074   78.05706

Jan 2008 to Dec 2015

Forecast Horizon      ER        WPI        CPI

1                  1.58152    35.61277   54.90210
2                  7.345030   28.73818   49.12083
3                  9.443049   26.54351   41.77180
4                  9.094853   25.83251   41.26593
5                  9.421373   29.70301   37.73859
6                  9.016958   29.20987   38.71932
7                  9.977859   29.10490   38.07151
8                  9.894556   29.44591   37.75319
9                  10.61339   29.51644   37.18571
10                 10.59596   29.62499   37.08092
11                 10.60075   29.61638   36.96253
12                 10.62261   29.64721   36.87982

Table 5
Variance Decomposition of WPI Prices

Aug 1991 to Dec 2007

Forecast Horizon     OIL         MP        GM2

1                  5.364727   1.851496   0.210479
2                  5.903404   1.738666   0.495032
3                  5.797335   1.697026   0.506460
4                  5.603704   3.391264   0.896031
5                  5.853440   3.368468   0.901773
6                  5.824016   3.370724   0.910513
7                  5.808169   3.360617   0.947762
8                  5.810144   3.361070   0.972510
9                  5.803994   3.367367   1.007508
10                 5.802770   3.365809   1.038452
11                 5.801600   3.365143   1.049841
12                 5.800646   3.364602   1.062450

Jan 2008 to Dec 2015

Forecast Horizon     OIL         MP        GM2

1                  27.14274   0.094908   1.425209
2                  33.23082   0.717995   1.576516
3                  36.47614   4.124007   1.261701
4                  38.06105   4.616462   1.342800
5                  36.00459   4.496149   1.562806
6                  35.87984   4.482813   1.555006
7                  34.95048   4.456977   1.513993
8                  34.79349   4.592939   1.546633
9                  34.79547   4.633054   1.568612
10                 34.39376   4.622834   1.648461
11                 34.30451   4.614356   1.670715
12                 34.29194   4.613690   1.671535

Aug 1991 to Dec 2007

Forecast Horizon      ER        WPI        CPI

1                  0.784872   91.78843   0.000000
2                  0.722550   91.12597   0.014375
3                  1.281178   90.41472   0.303280
4                  1.538773   87.40966   1.160564
5                  1.730092   86.80509   1.341137
6                  1.793697   86.31038   1.790670
7                  1.901328   86.09086   1.891260
8                  1.926616   86.02797   1.901691
9                  1.924366   85.92706   1.969709
10                 1.923519   85.88649   1.982958
11                 1.926718   85.86831   1.988385
12                 1.928046   85.85482   1.989435

Jan 2008 to Dec 2015

Forecast Horizon      ER        WPI        CPI

1                  0.150670   71.18647   0.000000
2                  3.043955   60.43077   0.999945
3                  3.751112   53.01656   1.370475
4                  5.461830   47.23486   3.283004
5                  7.302691   47.18786   3.445908
6                  7.296734   47.07606   3.709542
7                  9.116309   46.18338   3.778861
8                  9.324049   45.97971   3.763181
9                  9.640115   45.66285   3.899899
10                 9.653236   45.39743   4.284288
11                 9.701978   45.27939   4.429047
12                 9.720319   45.26497   4.437541

Table 6
Positive and Negative Estimated Cumulative
Pass-through Coefficient of Domestic Prices

Periods      OIL+       OIL-             OIL+       OIL-

1            0.201499   0.202281         0.311680   0.200348
2            0.285391   0.274787         0.505916   0.404133
3            0.353978   0.378861         0.620749   0.389477
4            0.457849   0.316329         0.737538   0.260069
5            0.502842   0.307053         0.777660   0.187483
6      CPI   0.534440   0.304815   WPI   0.845955   0.085166
7            0.602899   0.313308         0.938488   0.061661
8            0.663207   0.310396         1.010799   0.053445
9            0.683872   0.318802         1.048130   0.064181
10           0.709990   0.292314         1.077952   0.038117
11           0.734919   0.276050         1.104904   0.004298
12           0.751122   0.272664         1.127705   -0.023468

Table 7
Variance Decomposition of Domestic Prices

Periods      OIL+       OIL-             OIL+       OIL-

1            10.22608   9.448728         10.91161   6.045167
2            10.99424   10.23543         12.15023   10.61722
3            11.23407   11.51202         12.70054   10.31799
4            12.66214   11.63310         13.39501   11.10738
5            12.35937   11.16849         13.12069   10.59984
6      CPI   12.30861   11.06806   WPI   13.31992   11.52646
7            13.03060   11.05130         13.87186   11.53124
8            13.48083   11.02409         14.14864   11.24969
9            13.49583   10.97579         14.20698   11.24659
10           13.55853   11.07136         14.23403   11.29555
11           13.62643   11.10823         14.25936   11.37443
12           13.65354   11.10043         14.28426   11.42931

Talah Numan Khan <talahnuman@yahoo.com> is PhD Scholar, School of Economic Sciences, Federal Urdu University of Arts, Science and Technology, Islamabad. Wasim Shahid Malik <wsmalick@gmail.com> is Associate Professor, School of Economics, Quaid-i-Azam University, Islamabad.

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Caption: Fig. 1. Crude Oil Prices

Caption: Fig. 2. Impulse Responses of Domestic Prices to Oil Prices

Caption: Figure 3: Impulse Responses of CPI

Caption: Fig. 4. Impulse Responses of WPI

Caption: Fig. 5. Impulse Responses of CPI to Oil (+)and Oil(-) Prices

Caption: Fig. 6. Impulse Responses of WPIto Oil(+) and Oil(-) Prices

Table 1
Augmented Dickey Fuller Test (Unit Root)

Variables      Level     First Difference   Order of Integration

LOIL         -1.227520      -5.883818               I(1)
LMP          -0.639713      -6.901239               I(1)
LER          -2.107194      -18.41848               I(1)
LM2          -2.006917      -5.035952               I(1)
LWPI         -0.955668      -3.029067               I(1)
LCPI         0.090244       -3.899993               I(1)

Table 2
Estimated Cumulative Pass-through Coefficient of Domestic Prices

CDI

Periods   Aug91 to Dec2015   Aug91 to Dec2007    Jan2008 to Dec2015

1             0.084379           0.059790             0.149851
2             0.174758           0.088914             0.330826
3             0.237488           0.094564             0.519546
4             0.254950           0.045185             0.632414
5             0.269878           0.059204             0.659225
6             0.319320           0.069652             0.736362
7             0.323170           0.066184             0.743210
8             0.326652           0.065609             0.785260
9             0.338346           0.064619             0.762973
10            0.345877           0.067340             0.785847
11            0.348989           0.066226             0.825061
12            0.351012           0.066825             0.840808

WPI

Periods   Aug91 to Dec2015   Aug91  to Dec2007   Jan2008 to Dec2015

1             0.322818           0.194306             0.538782
2             0.575948           0.282421             0.984248
3             0.708844           0.260074             1.410428
4             0.834701           0.241980             1.794736
5             0.896502           0.291078             1.842427
6             0.928907           0.297895             1.889758
7             0.928632           0.294028             1.866545
8             0.932593           0.301449             1.890222
9             0.939146           0.303875             1.858596
10            0.933919           0.300202             1.848036
11            0.934S35           0.299300             1.852333
12            0.934729           0.299208             1.858539

Table 3
Variance Decomposition of Domestic Prices

CPI Inflation

Forecast Horizon     OIL         MP        GM2

1                  1.786834   0.004530   0.014535
2                  3.644916   0.014099   0.190148
3                  4.220795   0.852106   0.686636
4                  4.037449   0.802904   1.450440
5                  4.003503   0.819025   1.422131
6                  4.440563   0.810860   1.575015
7                  4.421472   0.851564   1.686657
8                  4.408573   0.851741   1.754805
9                  4.428049   0.853107   1.774949
10                 4.434050   0.853027   1.821669
11                 4.433935   0.852714   1.835419
12                 4.433844   0.852861   1.845059

WPI Inflation

Forecast Horizon     OIL         MP        GM2

1                  12.44110   0.661758   0.024306
2                  16.58452   0.746426   0.590821
3                  17.01089   0.795893   0.561197
4                  17.49865   1.416897   1.007546
5                  17.59650   1.400135   1.000996
6                  17.61036   1.396261   1.131320
7                  17.57730   1.394078   1.150280
8                  17.54030   1.394199   1.192524
9                  17.53459   1.393469   1.209802
10                 17.52736   1.393853   1.222949
11                 17.52199   1.395256   1.225625
12                 17.51704   1.395651   1.227219

CPI Inflation

Forecast Horizon      ER        WPI        CPI

1                  0.542424   20.50932   77.14235
2                  2.215229   20.59857   73.33704
3                  4.282615   22.37695   67.58090
4                  4.037131   22.70550   67.56657
5                  4.104025   22.47717   67.17415
6                  4.060683   22.44539   66.66749
7                  4.040707   22.52895   66.47065
8                  4.027825   22.55427   66.40279
9                  4.028192   22.54726   66.36844
10                 4.026675   22.54698   66.31760
11                 4.024833   22.54870   66.30440
12                 4.025185   22.54722   66.29583

WPI Inflation

Forecast Horizon      ER        WPI        CPI

1                  0.440630   86.43220   0.000000
2                  0.616084   8135061    0.111543
3                  0.643528   80.47366   0.514836
4                  0.667694   77.94290   1.466312
5                  1.150479   77.38501   1.466878
6                  1.203170   77.13229   1.526597
7                  1.340611   76.99540   1.542332
8                  1.476833   76.82813   1.548015
9                  1.502279   76.78953   1.570325
10                 1.522694   76.75453   1.578621
11                 1.531635   76.73585   1.589641
12                 1.537011   76.72263   1.600449