Understanding the Crude Oil Price: How Important Is … · Understanding the Crude Oil Price: ......

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The Energy Journal, Vol. 32, No. 4. Copyright �2011 by the IAEE. All rights reserved.

* Corresponding author. Center for Energy, Petroleum and Mineral Law and Policy, Universityof Dundee, Carnegie Building, Dundee, DD1 4HN, UK. E-mail: [email protected].

** School of Economics, Shanghai University of Finance and Economics, 777 Guoding Road,Shanghai 200433, China. E-mail: [email protected].

We thank Kevin Forbs, Ian Lange, Zhen Zhu, two anonymous referees, the journal editor James Smithand other seminar participants at University of Stirling and University of Dundee for helpful com-ments, Audrey McLaughlin for helping us proofread the paper. Xiaoyi acknowledges the travel grantfrom the Carnegie Trust. All errors remain our own.doi: 10.5547/ISSN0195-6574-EJ-Vol32-No4-4

Understanding the Crude Oil Price: How Important Is theChina Factor?

Xiaoyi Mu* and Haichun Ye**

This paper employs monthly data on China’s net oil import from January1997 to June 2010 to assess the role of China’s net import in the evolution of thecrude oil price. Based on a vector autoregression (VAR) analysis, we find thatthe growth of China’s net oil import has no significant impact on monthly oilprice changes and there is no Granger causality between the two variables. Thehistorical decomposition indicates that shocks to China’s oil demand have onlyplayed a small role in the oil price run-up of 2002–2008. We also calculate theprice changes implied by China’s net oil import growth from a longer-term supplyand demand shift perspective. doi: 10.5547/ISSN0195-6574-EJ-Vol32-No4-4

“Surging Chinese demand is underpinning the recent spike inthe price of oil, figures from the International Energy Agency(IEA) show. This ‘China factor’ has more bearing on oil pricesthan the ‘risk factor’ coming from global tensions, some expertssay”

—CNN (2004)

“The price of crude oil could soar to $200 a barrel in as littleas six months, as supply continues to struggle to meet demand. . . Soaring global demand for oil is being led by China’s

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continuing economic boom and, to a lesser extent, by India’srapid economic expansion.”

—BBC (2008)

1. INTRODUCTION

It is often asserted that the rising oil demand from China is one of themain reasons for the increase in oil prices over the period of 2002–2008. Indeedsince China became a net importer in the world oil market in 1993, China’s oilconsumption has risen quickly. Because the domestic oil production in China hasremained largely flat, the increase in consumption is mainly satisfied by increasesin import. Figure 1 displays China’s net oil import which includes the net importin both crude oil and refined petroleum products from January 1997 to June 2010.During this period, China’s net import has increased by almost five times withan annual average growth rate of 15.75 percent. It is probably this rapid growthin China’s oil import that has attracted much attention from the media. Also shownin Figure 1 is the front month futures price of West Texas Intermediate (WTI)which is deflated using the US consumer price index(CPI) and expressed in Jan-uary 2009 levels. While both series appear to have a common upward trend, howmuch China’s import has contributed to the world oil price remains an openquestion.

A systematic examination of the relationship between China’s importand oil prices in the world market can help us disentangle various factors behindoil price changes. The surge in crude oil prices from 2002 to mid-2008 has spurreda new wave of heated debate over the causes and consequences of the oil priceshocks. Some of the significant contributions in the academic literature includeKilian (2009), Smith (2009) and Hamilton (2009a, 2009b). Kilian (2009) distin-guishes oil price shocks between oil supply shocks, aggregate demand shocks,and precautionary demand shocks that are specific to the oil industry and arguesthat the recent oil price run-up until mid-2008 is primarily driven by boomingaggregate demand. He finds that the demand-driven shocks have very differenteffects on the real price of oil and tend to impact the real economic activitydifferently from supply-driven oil price shocks. Hamilton (2009a) reviews severalstrands of theories about oil prices including the cash-and-carry model, the futuresmarket theory and Hotelling’s scarcity rent theory and relates them to statisticalevidence. He concludes that the scarcity rent may have started to become animportant factor in the price of crude oil owing to the strong demand growthfrom China, the Middle East and other emerging economies. Hamilton (2009b)analyzed the causes and consequences of the oil price shock of 2007–2008 andargues that it was caused primarily by a combination of strong demand growthand stagnating production. Smith (2009) analyzes the global demand shift, non-OPEC and OPEC supply shifts relative to 1973–1975 levels and concludes thata substantial part of the oil price rise since 2004 can be explained by a combinationof unexpected demand growth from China and other developing nations and a

Understanding the Crude Oil Price: The China Factor / 71

Figure 1: China’s Net Oil Import and the Real Price of Oil:1997M1–2010M6

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Notes: WTI is the front-month futures price of West Texas Intermediate. The real oil price is obtainedby deflating WTI by the U.S. consumer price index (January 2009�100). CNIMP denotes China’snet import of both crude oil and refined products. The solid line denotes the real price of oil. Thedotted line denotes China’s net oil import.

negative shift in oil supply due to higher factor costs. While both Hamilton(2009a, 2009b) and Smith (2009) argue that the demand growth from China hasbeen an important factor, neither attempted to assess the relative importance ofthe “China factor” in oil prices.

This paper employs monthly time series data on China’s net oil importand the international benchmark crude oil prices over the period of January 1997to June 2010 to assess the role of China’s demand growth in the world oil pricerun-up. We focus on China’s net import for two reasons. First, data on net importis readily available. Chinese Customs typically releases the data on monthly im-port and export of crude oil and refined petroleum products within two weeksafter the end of each month. As there is no official statistics on oil inventorychanges at monthly or weekly levels in China, the import and export statisticshas become almost the most important single barometer for industry analysts andtraders to gauge China’s oil demand. Second, since China is a net importer in theworld oil market during this period, changes in Chinese net import effectivelyrepresent demand changes in the world oil market.

In the first part of our empirical analysis, we estimate a vector autore-gression (VAR) model and perform impulse response analysis, forecast error var-iance decomposition and historical decomposition to investigate the interactionbetween China’s oil demand and the real price of oil. In addition, we also conduct


1. The futures price data used in this study is highly correlated with the U.S. refiners’ acquisitioncost for imported crude oil used by Kilian (2009). The sample correlation coefficient is 0.998. UsingBai and Perron’s (1998) methodology for multiple structural changes, we find no evidence of structuralbreaks in the logged real oil price during the sample period.

out-of-sample Granger causality tests to examine the causal relationship betweenChina’s oil demand and the real oil price. In general, our results suggest that thegrowth rate of China’s net oil import has only a small impact on the real oil priceand that there is no Granger causality between the two variables. The second partof our analysis, from a longer-term demand and supply shift perspective, answersthe question how much price change is required in order to increase the crude oilsupply to meet China’s growing demand based on plausible estimates of priceelasticity of crude oil supply. The result indicates, on average, the growth inChina’s net oil import has contributed to about 11–23 percent of the price increasebetween 2002 and mid-2010 depending on assumed supply elasticities. Notably,both the historical decomposition from the VAR analysis and the longer-termdemand and supply shift analysis suggest that the share of the real oil price changeattributable to China’s demand growth is lower in the price spike of 2008 thanthe average estimates for the full sample period.

The rest of the paper is organized as follows. Section 2 describes ourdata and reviews the empirical methodology used in this study. Section 3 reportsthe empirical results from our VAR analyses. In Section 4 we conduct the longer-term demand and supply shift analysis. Section 5 offers concluding remarks.

2. DATA AND EMPIRICAL METHODOLOGY

2.1 Data

The nominal oil price data is the monthly averages of the daily settlementprice of WTI front-month futures and obtained from the Energy InformationAdministration (hereafter, EIA) of the US Department of Energy.1 We deflate thenominal oil price by the US consumer price index (CPI) and express it at theJanuary 2009 level. The unit root tests in Table 1 indicate that the logged real oilprice is stationary when a deterministic trend is included. We thus remove thelinear trend from the series and use the detrended logged real oil price (DTLRWTI)in our empirical model.

We obtain China’s net import of crude oil and refined petroleum products(liquid products only, measured in million barrels per day) from the GeneralAdministration of Customs of China. The data spans the period January 1997–June 2010. As shown in Table 1, while the augmented Dickey-Fuller (ADF) andthe Elliott-Rothenberg-Stock DF-GLS (DF-GLS) tests find some weak evidenceof the series being trend-stationary at the 10% level, the Ng-Perron and the Kwiat-kowski-Phillips-Schmidt-Shin (KPSS) tests show that the series is a unit rootprocess rather than trend-stationary. In this study we use the year-over-year


Table 1: Unit Root Tests

Variable ADF DF-GLS NP KPSS

Real oil price Level without trend –1.717 –1.082 –1.621 1.258***Level with trend –3.433** –3.101** –25.737*** 0.092First difference –6.586*** –5.319*** –28.128*** 0.040

China’s net oil import Level without trend –0.726 1.556 1.144 1.390***Level with trend –3.372* –2.927* –4.267 0.303***First difference –6.908*** –0.323 –0.750 0.163Seasonal difference –5.771*** –1.874* –7.503* 0.134

World oil production Level without trend –0.985 0.441 –0.445 1.319***Level with trend –2.037 –2.046 –7.117 0.169**First difference –11.010*** –1.998** 11.341** 0.044

Notes: The sample period is Jan 1998–Jun 2010. All variables are in natural logarithms. ADF, DF-GLS, NP and KPSS refer to the augmented Dickey-Fuller test statistic, the Elliott-Rothenberg-StockDF-GLS test statistic, the Ng-Perron test statistic and the Kwiatkowski-Phillips-Schmidt-Shin teststatistic, respectively. The null hypothesis in the ADF, DF-GLS and NP tests is that the series has aunit root while the null hypothesis in the KPSS test is that the series is stationary. Lag lengths in theADF, DF-GLS and NP tests are selected by the Akaike information criterion (AIC). The superscripts,***, **, and *, denote the rejection of the null at the significance levels of 1%, 5% and 10%,respectively.The seasonal difference of China’s net oil import is the log difference of China’s net oil import betweenmonth t and month t-12.The time trend variable in logged real oil price is statistically significant at the 1% level in the ADFtest.

2. In addition, we also applied Johansen’s cointegration test on the logged oil price and the loggedChina’s net oil import while allowing for a linear deterministic trend, and found no evidence thatthese two series were cointegrated. Detailed results from Johansen’s cointegration test are not reportedbut available upon request.

growth rate of China’s net oil import (CNIMPG, referred to as seasonal differencein Table 1), which is defined as the log difference of China’s net oil importbetween month t and month t-12.2 As such, we lose the first 12 observations andthe sample period used in our main empirical analysis runs from January 1998 toJune 2010.

To control the possibility that changes in crude oil supply drive therelationship between China’ net oil import and the oil price, we include the per-centage change in world crude oil production (WDPROG) in our empirical anal-ysis. The world crude oil production data (measured in thousands barrels per day)is also available from the EIA. As evident in Table 1, both the world oil productiongrowth and China’s net oil import growth are covariance stationary.

2.2 Empirical Model

To analyze the dynamic relationship between China’s net oil importgrowth and the real price of oil, we estimate a three-variable vector autoregression(VAR) model over the entire sample period as follows:


3. An example of oil supply shocks could be a disruption of oil production in one of the oilproducing countries such as Nigeria.

4. This restriction is plausible for two reasons. First, over the study period, the price of crude oilsold domestically in China is indexed to international benchmark oil prices with one month’s lag; theinternational oil price is unlikely to have an immediate impact on China’s import. Second, it takesseveral weeks to ship oil from Middle East and western Africa, where China imports most of thecrude oil, to China. Even if the international oil price has an immediate impact on China’s importdecision, the cargo is unlikely to arrive at a Chinese port and to be accounted in the customs’ statisticswithin a month.

3

BY �A � A Y �e (1)t 0 � i t– i ti�1

where and et is a vector of structuralY �(WDPROG ,CNIMPG ,DTLRWTI )�t t t t

innovations. Based on the Akaike Information Criterion (AIC), we use three lagsin the VAR model.

Pre-multiplied equation (1) by B–1 and let ut denote the reduced-formVAR residuals such that ut� B–1et. The structural innovations et can be recoveredfrom the reduced form residuals ut by imposing restrictions on B–1. FollowingKillian’s (2009) identification strategy, we apply the Cholesky decomposition tothe reduced-form residuals with the variables ordering WDPROGt ,CNIMPGt ,DTLRWTIt . That is,

WDPROG oil supply shocku u 0 0 et 11 t

CNIMPG China’s oil demand shocku � u � u u 0 e (2)t t 21 22 t� � � �� DTLRWTI other oil demand shocku u u u et 31 32 33 t

In this model, first, we assume that the world oil production growth contempo-raneously responds to only its own shocks (hereafter referred to as “oil supplyshocks”).3 Due to the long-lead time and capital intensive nature of petroleumproduction projects, the price elasticity of crude oil supply in the short-term isextremely low. Thus, it is reasonable to assume that crude oil production doesnot respond to innovations in demand and prices within the same month. Second,we assume that China’s net oil import growth is contemporaneously affected byonly oil supply shocks and shocks to China’s oil demand (referred to as “China’soil demand shock” hereafter), but not shocks to international crude oil prices.4

Last, we refer innovations to the real oil price that cannot be explained by eitheroil supply shock or China’s oil demand shocks as other demand shocks, whichpotentially represent all other countries’ oil demand shocks and the “precautionarydemand” shocks referred to by Kilian (2009). We assume that the real oil priceresponds contemporaneously to all three types of shocks including oil supplyshocks, China’s demand shocks and also other demand shocks.


5. We believe our indentifying restrictions in equation (2) are reasonable. Nevertheless, our resultsfrom the impulse response function and variance decomposition are robust to different orderings ofthe variables.

6. We also conducted the in-sample F test for Granger causality between China’s net oil importgrowth and real oil price based on the baseline VAR model and found no evidence for causationbetween these two variables.

Based on these identified shocks, we then employ impulse responsesanalysis, forecast error variance decomposition, and also historical decompositionto investigate how the real price of oil is impacted by each of these shocks.5

2.3 Out-of-sample Granger Causality Tests

To further understand the role of China’s oil demand in the evolution ofthe real oil price, we also conduct out-of-sample tests to check whether China’snet oil import growth Granger causes changes in the real oil price. While the in-sample Granger causality tests have been widely employed in previous studieson causality, the value of in-sample evidence of Granger causality may not bevery reliable in the sense that it could simply be an artifact of the specificationsearches used in obtaining empirical models. In contrast, as pointed out by Ashleyet al. (1980), an out-of sample comparison of forecasting performance can yieldthe maximum amount of information that is relevant to the hypothesis of Grangercausation and thus is more in the spirit of the definition of Granger causality.Thus, we employ out-of-sample Granger causality tests here to study the causallinkage between China’s net oil import growth and the real oil price. 6

The out-of-sample tests for Granger causality from China’ net oil importgrowth to the detrended log real price of oil are implemented in two steps. In thefirst step, we estimate both the restricted and unrestricted models for the real oilprice. Specifically, the unrestricted model for the real oil price is simply the lastequation in the VAR(3) model (Eq. (1)) that has the detrended log of the real oilprice as the dependent variable, while the restricted model for the real oil priceis the unrestricted model without the lagged values of China’s net oil importgrowth variable. In the second step, formal statistical tests are employed to ex-amine whether the out-of-sample mean squared forecast errors (MSFE) from theunrestricted model are smaller than those obtained from the restricted one. If theunrestricted model for the real oil price improves forecast accuracy over the re-stricted model by yielding significantly smaller MSFE, China’s net oil importgrowth is said to have predictive power for the real oil price, and thus is consid-ered to be evidence for China’s net oil import growth Granger causing movementsin the real oil price. Granger causality from the real oil price to China’s net oilimport growth is tested similarly.

In this study we consider five out-of-sample tests recently developed inthe literature of forecast evaluation: the Granger-Newbold (1976) test, the Die-


7. See Ashley and Ye (2010) for detailed review of these five out-of-sample Granger causalitytests.

8. We also applied the recursive-design wild bootstrap proposed by Goncalves and Killian (2004)and obtained very similar results.

9. For simplicity, we fix the values of initial observations at their actual sample values.

bold-Mariano (1995) test, Clark-West (2006, 2007) test, McCracken’s (2007)MSE-F test, and also the Clark-McCracken’s (2001) ENC-NEW test.7

2.4 The Bootstrap Method

Given that we have a relatively small sample, the statistical inferencebased on asymptotic distributions may be problematic. We use the bootstrappingmethod (with 5000 replications) to obtain the confidence intervals for impulseresponses and forecast error variance decompositions, and the p–values for re-jecting the null hypothesis of equal MSFE for the restricted and unrestrictedmodels.8

Let , t�1, . . ,T, denote the OLS residuals from the VAR(3) model (Eq.ut

(1)). We first compute the centered residuals as , where , and–1u – u u�T ut � t

obtain bootstrap residuals , . . , by randomly drawing with replacement from* *u u1 T

the centered residuals. From these bootstrap residuals, we then construct artificialtime series of world oil production growth (WDPROG), China’s net oil importgrowth (CNIMPG) and the detrended log of the real oil price (DTLRWTI) usingthe VAR(3) as the bootstrap data generating process (DGP).9

For each of these 5000 artificial datasets, we then construct the 95 (68)percent confidence intervals for the impulse responses and forecast error variancedecomposition using the 2.5th (16th) and the 97.5th (84th) percentiles of theirempirical distributions as lower and upper bounds, respectively. The p-value foreach of the out-of-sample Granger causality test statistics is calculated as theproportion of the generated test statistic values exceeding the test statistic valueobtained using the actual sample data.

3. EMPIRICAL RESULTS FROM VAR

3.1 Results from Innovation Accounting

Figure 2 graphs the point estimates of impulse responses of the real oilprice to one-standard-deviation structural shocks along with their bootstrapped95 percent and 68 percent confidence intervals. Given a positive one-standarddeviation structural shock to China’s oil demand (i.e. raising China’s net oil im-port), the real oil price first rises for about seven months and then declines grad-ually. Six months after shock, the real oil price rises by about 1.5 percent. Basedon the bootstrapped 95 percent confidence intervals, however, the positive impactof China’s demand shock on the real price of oil is not statistically significant at


Figure 2: Impulse Responses of the Real Oil Price: Baseline VAR(3) Model

Panel A. Responses of the real oil price to one-standard deviation oil supplyshock

Panel B. Responses of the real oil price to one-standard deviation China’s oildemand shock

Panel C. Response of the real oil price to one-standard deviation other demandshock

Notes: The horizontal axis indicates the time horizon in terms of months after shocks. The verticalaxis shows the changes in the logarithm of the real oil price. The solid line denotes the point estimatesof impulse responses. The dashed lines and the dotted lines denote the bootstrapped 95 percent and68 percent confidence intervals for the impulse responses based on 5000 replications, respectively.


Table 2: Forecast Error Variance Decomposition for the Real Oil Price:Baseline VAR(3) Model

ForecastingHorizon (Months) Oil supply shock China’s demand shock Other demand shocks

3 0.227[0.084, 8.508]

0.537[0.156, 10.287]

99.236[86.255, 99.430]

6 0.188[0.103, 9.559]

1.400[0.206, 17.785]

98.412[79.162, 99.310]

9 0.174[0.116, 10.079]

2.230[0.237, 22.800]

97.596[74.125, 99.192]

12 0.183[0.124, 10.143]

2.677[0.251, 24.873]

97.137[72.481, 99.163]

15 0.198[0.127, 10.154]

2.837[0.258, 25.545]

96.966[72.017, 99.143]

Notes: The percentage share of total forecast error variance of the real oil price (in logarithm, de-trended) attributed to each one of the three structural shocks at horizon h is obtained from the estimatedVAR(3) model that include world oil production, China’s net oil import growth and the logged realoil price (detrended). Their 95% confidence intervals are constructed using the bootstrap method with5000 replications and reported in brackets.

10. Based on the 68 percent confidence intervals, we notice that the impulse responses of real oilprice to China’s demand shock are statistically significant, at the 32 percent level, between the 9thmonth and 11th month after the shock.

the five percent level.10 With respect to positive oil supply shocks (i.e. raisingworld oil production), the real oil price drops slightly for the first seven months,with a maximum fall of 0.75 percent two months after the shock. The 95 percentconfidence intervals for the responses of the real oil price to oil supply shocks,yet again, suggest that the negative effect of oil supply shocks on the real oilprice is statistically insignificant. The last panel of Figure 2 shows the responseof oil price to other oil demand shocks. With a positive one-standard deviationshock, the real oil price rises on impact for roughly eight months. The real priceof oil is expected to increase by 10 percent or so within the first three monthsand then the positive impact starts to diminish slowly. As indicated by the 95percent confidence intervals, the positive impact of other oil demand shocks onthe real oil price is statistically significant at the five percent level.

In Table 2 we report the percentage contributions of the three identifiedshocks to the forecast error variance of the real oil price at various horizons.China’s oil demand shock turns out to have limited explanatory power for themovements in the real oil price. The proportion of the real oil price variationaccounted for by China’s oil demand shocks at the one-year horizon is 2.68percent, with the 95 percent confidence interval extending from 0.25 percent to24.87 percent. Over time the proportion of forecast error variance of the real oilprice due to China’s oil demand shock rises slightly but remains less than three


Figure 3: Historical Decomposition of the Real Oil Price: Baseline VAR(3)Model

Notes: The horizontal axis indicates the time period. The vertical axis indicates the historical contri-bution of each of the three shocks to the logged real oil price. The dotted line indicates the cumulativeeffect of oil supply shocks on the real oil price. The solid line indicates the cumulative effect ofChina’s oil demand shocks on the real oil price. The dashed line indicates the cumulative effect ofother demand shocks on the real oil price.

percent at any time horizons. As compared to China’s oil demand shock, oilsupply shock explains even less amount of forecast error variance of the real oilprice. The proportion of forecast error variance contributed by oil supply shockis around 0.2 percent at any horizons. The majority of the variation in the real oilprice, unsurprisingly, is induced by other oil demand shock. It accounts for about99 percent of the variation in the real oil price at the three-month horizon, withthe 95 percent confidence interval extending from 86.26 percent to 99.43 percent.As time passes, its explanatory power for the movements in the real oil pricedecreases somewhat yet still remains at the level of around 97 percent.

To gain further insight into the effects of the three identified shocks onthe behaviour of the real oil price over time, we plot in Figure 3 the historicalcontributions the three shocks have made to fluctuations in the real price of oil.Among the three shocks, oil supply shock has made the smallest contribution tothe fluctuations in the real oil price, with a size of less than three percent changein the real oil price. Nonetheless, the result is consistent with Kilian’s (2009)finding. As compared to the oil supply shock, China’s oil demand shock has hada slightly larger effect on the evolution of the real oil price. After the 1997 Asian


financial crisis (i.e. 1998–1999), China’s oil demand shock induced a fall in thereal oil price of roughly 10 percent. The largest positive effect of China’s oildemand shock on the real oil price is observed in the middle of year 2000. FromNovember 1999 to July 2000, China’s oil demand shock contributed to an increaseof over 10 percent in the real oil price. Between the year 2002 and 2005, the realoil price experienced small increases, not more than five percent, due to China’sdemand shock. When the real oil price spiked from mid-2007 to mid-2008,China’s oil demand shock actually lowered the real oil price from its trend byfive percent. From late 2008 to 2009, China’s oil demand has quickly recovered,perhaps as a result of its stimulus packages, and helped pull up the real oil priceby three to four percent. Consistent with our impulse responses and forecast errorvariance decomposition analysis, the biggest contribution to the evolution of thereal oil price is from other oil demand shocks. During the period Jan 2007–mid2008, other oil demand shocks caused the real oil price to rise dramatically byabout 65 percent.

3.2 Results from Out-of-sample Granger Causality Tests

To investigate the causal relationship between China’s net oil importgrowth and the real price of oil, we perform a variety of out-of-sample Grangercausality tests based on the VAR(3) model (Eq. (1)). The first three observationsin the year of 1998 are reserved for creating lagged variables. The 81 sampleobservations from April 1998 to December 2004 are used as the in-sample periodfor model estimation, and the remaining 66 observations over the period fromJanuary 2005 to June 2010 are reserved as the out-of-sample period for forecastaccuracy evaluation. In particular, we use recursive one-step-ahead forecast errorsin the forecast accuracy evaluation.

Table 3 reports the sample test statistics along with their p-values fromthe out-of-sample tests of Granger causality between China’s net oil importgrowth and the real oil price. The left column presents the testing results for thenull hypothesis of no Granger causality from China’s net oil import growth to thereal oil price, and the right column shows the results for the null hypothesis ofno Granger causality from the real oil price to China’s net oil import growth. Thereported p-values are for rejecting the null hypothesis and are obtained using thebootstrapped sampling distributions of the listed test statistics. A p-value less thanfive (ten) percent means that the null hypothesis of no Granger causality can berejected and thus there is evidence in favour of Granger causality running fromone variable to the other at the significance level of five (ten) percent.

With regard to the null hypothesis of nonexistence of Granger causalityfrom China’s net oil import growth to the real oil price, none of the out-of-sampletest statistics are significant at the ten percent level, indicating that China’s netoil import growth does not have a significant amount of predictive power for themovement of the real oil price. Thus, there is no out-of-sample evidence forChina’s net oil import growth Granger causing changes in the real oil price. The


Table 3: Out-of-sample Granger-causality Tests: Baseline VAR(3) Model

H0: No Granger causalityfrom CNIMPG to

DTLRWTI

H0: No Granger causalityfrom DTLRWTI to

CNIMPG

Granger-Newbold (GN) test –0.7614(0.7113)

–2.0230(0.9874)

Diebold-Mariano (DM) test –0.6087(0.6395)

–1.3988(0.9464)

Clark-West (CW) test –0.2465(0.7904)

0.7790(0.6995)

McCracken (MSE-F) test –1.3327(0.5827)

–16.8070(0.9998)

Clark-McCracken (ENC-NEW) test –0.2435(0.7766)

4.1626(0.3369)

Notes: Here IMPG denotes the growth rate of China’s net oil import, and DTLRWTI denotes thedeviation of the logged real oil price from its linear trend. The in-sample period is 1998M4–2004M12with a total of 81 observations. The out-of-sample period is 2005M1–2010M6 with a total of 66observations. Sample statistics are reported and their bootstrapped p-values are reported in parenthe-ses.

11. For example, Platts releases its monthly calculation of China’s apparent demand between the18th and 26th of every month via press release and via its website. In a news report on August 25,2010, Bloomberg reports “China’s apparent crude demand growth may slow ‘noticeably’ in the thirdquarter”. We are aware that Table 3a of the EIA short-term energy outlook contains data on China’soil consumption at monthly frequencies. However, a closer look reveals that until 2003 the data appearto be derived from quarterly statistics as the monthly numbers within a quarter are all identical before2004.

results are fairly similar with respect to Granger causality running from the realoil price to China’s net oil import growth. Again, none of the test statistics arestatistically significant at the ten percent level, meaning that there is no Grangercausality from the real oil price to China’s net oil import growth, either.

3.3 Robustness Checks

A potential concern over the above analysis is that we didn’t considerthe impact of demand growth from the rest of the world. In this subsection weevaluate the robustness of our findings regarding the relationship between China’soil demand and the real oil price using alternative model specifications. Onerobustness check is to use China’s share of oil consumption in the world to replaceChina’s net oil import growth. The monthly world consumption data is also avail-able from the EIA. Since there are no official statistics on oil inventory changesat monthly frequencies in China, we follow practices by many industry analystsand compute China’s oil consumption share of the world on the basis of China’sapparent consumption which is the sum of domestic production and net import.11


Figure 4: Impulse Responses of the Real Oil Price: Using China’s OilConsumption Share

Panel A. Responses of the real oil price to one-standard deviation oil supplyshock

Panel B. Responses of the real oil price to one-standard deviation China’s oildemand shock

Panel C. Response of the real oil price to one-standard deviation other oildemand shock



Figure 5: Impulse Responses of the Real Oil Price: Including ROWConsumption Growth


12. The monthly series of China’s oil consumption share of the world starts from January 1997.Unit root tests finds that China’s oil consumption share of the world is trend stationary. We thus useits detrended component in our VAR analysis. On the basis of AIC, two lags are included in the VARmodel.

We then estimate a VAR(2) model over the period March 1997-June 2010 usingthe growth rate of world oil production (WDPROG), the detrended China’s oilconsumption share of the world (DTCNSHARE) and also the detrended log realoil price (DTLRWTI).12 Another robustness check is to see whether our mainresults are sensitive to the inclusion of oil consumption growth rate from the restof the world. The monthly data of oil consumption from the rest of the world(referred to as “ROW” for short) is also drawn from the EIA and its growth rateis added to our baseline VAR model. For each of these two VAR models, we thenconduct innovation accounting and out-of-sample Granger causality tests. In gen-


Figure 6: Historical Decomposition of the Real Oil Price: AlternativeSpecifications

Panel A. Using China’s oil consumption share: 1997M3–2010M6

Panel B. Including other countries’ oil consumption growth: 1998M1–2010M6

Notes: The horizontal axis indicates the time period. The vertical axis indicates the historical contri-bution of each of the identified shocks to the logged real oil price. The dotted line indicates thecumulative effect of the oil supply shock on the real oil price. The solid line indicates the cumulativeeffect of China’s oil demand shock on the real oil price. The bubbled line indicates the cumulativeeffect of the ROW oil demand shock on the real oil price. The dashed line indicates the cumulativeeffect of other demand shock on the real oil price.


Table 4: Forecast error variance decomposition for the real oil price:alternative specifications

Panel A. Using China’s oil consumption share

Forecasting Horizon(Months) Oil supply shock

China’s demandshock Other demand shocks

3 0.057[0.046, 6.359]

0.482[0.047, 8.423]

99.461[89.015, 99.671]

6 0.058[0.045, 6.924]

0.709[0.062, 13.444]

99.233[84.574, 99.626]

9 0.059[0.044, 7.033]

0.825[0.069, 15.943]

99.115[82.083, 99.622]

12 0.060[0.043, 7.067]

0.881[0.069, 16.940]

99.060[81.264, 99.611]

15 0.060[0.043, 7.068]

0.906[0.069, 17.439]

99.034[81.008, 99.611]

Panel B. Including other countries’ oil consumption growth

ForecastingHorizon (Months)

Oil supplyshock

ROW demandshock

China’sdemand shock

Other demandshocks

3 0.289[0.089, 8.639]

13.935[2.871, 31.480]

0.473[0.166, 9.814]

85.303[63.793, 93.650]

6 0.219[0.127, 9.751]

18.335[3.081, 42.572]

1.142[0.214, 16.389]

80.303[51.174, 91.948]

9 0.288[0.166, 10.333]

21.312[3.275, 48.554]

1.923[0.269, 21.061]

76.477[43.643, 90.907]

12 0.470[0.198, 10.583]

22.910[3.538, 50.624]

2.414[0.294, 22.249]

74.206[40.382, 90.390]

15 0.641[0.213, 10.662]

23.486[3.608, 50.980]

2.617[0.309, 22.482]

73.256[39.004, 90.236]

Notes: Bootstrapped 95% confidence intervals are obtained based on 5000 replications and reportedin brackets.

eral, our robustness checks yield similar results to those from the baseline model.That is, China’s oil demand shock has statistically insignificant impact on the realoil price, and there is no Granger causality at either direction between the twovariables.

Figures 4 and 5 exhibit the impulse responses of the real oil price tovarious shocks obtained from the VAR(2) model that uses China’s oil consump-tion share of the world and the VAR(3) model that includes ROW oil consumptiongrowth, respectively. In both cases the real oil price responds very little to China’soil demand shock. We also graph the historical decomposition of fluctuations inthe real oil price from the two VAR models in Panels A and B of Figure 6,respectively. Again, we observe that the contribution of China’s oil demand shockremain very small in both cases.

In Table 4 we report forecast error variance decomposition of the realoil price due to each of the identified shocks based on the two VAR models. While


Table 5 Out-of-sample Granger-causality Tests

Panel A. Using China’s oil consumption share

H0: No Granger causalityfrom DTCNSHARE to

DTLRWTI


DTCNSHARE

Granger-Newbold (GN) test –1.2019(0.7103)

–0.3153(0.7844)


–0.2581(0.7616)

Clark-West (CW) test –0.8809(0.7502)

1.0003(0.6693)


–1.3750(0.8040)

Clark-McCracken (ENC-NEW) test –0.5500(0.7265)

2.6506(0.5123)

Panel B. Including ROW oil consumption growth

H0: No Granger causalityfrom CNIMPG to

DTLRWTI


CNIMPG

Granger-Newbold (GN) test 0.2598(0.2721)

–0.7015(0.7956)


–0.7134(0.7934)

Clark-West (CW) test 0.1100(0.6433)

1.2680(0.4935)


–6.9843(0.9718)

Clark-McCracken (ENC-NEW) test 0.1003(0.6571)

6.4296(0.1460)

Notes: DTCNSHARE denotes the detrended China’s oil consumption share of the world, andDTLRWTI denotes the deviation of the logged real oil price from its linear trend. The in-sample periodis 1997M3–2004M12 with a total of 94 observations when China’s oil consumption share is used,and 1998M4–2004M12 with a total of 81 observations when the ROW oil consumption growth isincluded. The out-of-sample period is 2005M1–2010M6 with a total of 66 observations. Samplestatistics are reported and their bootstrapped p-values are reported in parentheses.

the explanatory power of China’s oil demand shocks for the variation in the realoil price obtained from the VAR(3) model that includes ROW oil consumptiongrowth is very similar to that from the baseline VAR model, the proportion ofvariation in the real oil price due to China’s oil demand shock becomes muchsmaller in the VAR(2) model that uses China’s oil consumption share of the world,accounting for less than one percent of the forecast error variance of the real oilprice at any horizons.

Table 5 reports the out-of-sample test results on Granger causality be-tween China’s oil consumption share of the world and the real oil price from thetwo VAR models. Again, there is no statistically significant evidence for a Granger


13. In the appendix, we give a detailed graphic exposition. Also see Smith (2009).14. We interpret five-year as an intermediate period as it takes about three to five years to develop

(e.g. drilling development wells and building production facilities) an oil field that has already beendiscovered.

causal relationship between the two variables as none of the test statistics havep-values lower than ten percent.

4. ANALYSIS OF LONGER-TERM DEMAND AND SUPPLY SHIFT

In the above analysis, we investigate the interaction between the growthrate of China’s net oil import and the deviation of the logged real oil price fromits linear trend. To the extent that China’s import has contributed to the trend ofoil prices, our statistical results may understate the impact of China’s oil import.After all, China’s import growth accounts for nearly 30 percent of the increasein world oil consumption between 2002 and 2008. To further evaluate the longer-term impact of China’s import growth on oil price changes, we calculate thepercentage changes in oil prices that are needed to bring supply up to meet China’sgrowing demand based on some plausible estimates of supply elasticity.

By definition of elasticity, for a positive demand shock to the global oil

market (DQw), to restore equilibrium the price must rise by where gs

DQ /Qw w

g –gs d

and gd denote the elasticity of supply and demand, respectively, and Qw is theequilibrium quantity demanded in the world. If China’s share in DQw is s, then

the price change attributable to China is . Since the observed changeDQ /Qw ws •

g –gs d

in China’s import should have incorporated the movement along the demandcurve, the change in oil price attributable to China’s import growth is

.13 Given reasonable estimates of supply and demand elasticities,DCNIMP/Qw

gs

we can quantify the price changes implied by China’s net import growth.Estimates of long-run supply elasticity typically range from 0.10 to 0.35.

For example, using an error correction model Krichene (2002) finds that the long-run supply elasticity of crude oil is 0.10 during 1973–1999. In an analysis on theimpact of China’s growing demand on US petroleum markets, the US Congres-sional Budget Office (hereafter, US CBO, 2006) adopts a five-year supply elas-ticity of 0.2. Smith (2009) uses 0.3 in his analysis of the long-run demand andsupply shifts in the oil market since 1970s. In our calculation, we assume thefive-year supply elasticity ranges between 0.2 and 0.1.14 In panel (a) of Figure 7,we show the range of price changes (in percentage) implied by the five-yearchanges in China’s net import from 2002 to June 2010. The lower and upperbounds correspond to supply elasticities of 0.2 and 0.1 respectively. To eliminatethe influence of seasonal variations, both DCNIMP and Qw are calculated on a12-month moving average basis. For example, the value of DCNIMP for June


Figure 7: Percentage Changes in Real Oil Prices

a. Price changes implied by China’s net oil import growth

Note: The lower bound indicates the price change implied by China’s net oil import growth over amoving five-year period when the supply elasticity of crude oil is equal to 0.2 and the upper bounddepicts the implied oil price changes when oil supply elasticity is equal to 0.1.

b. Historical changes of real oil prices

Notes: This vertical line depicts the percentage change in the moving average of the real oil price for12 months ending at each point relative to the moving average of the 12 months ending at the sametime five years ago.


15. If Qw is calculated in an arc elasticity fashion, that is, the average world consumption of thebeginning and the ending five-year period, the percentage in quantity DCNIMP/Qw would be smaller.

16. The correlation coefficient between the two series is 0.7.

2003 is the difference between China’s net import averaged over the 12 monthsending June 2003 and that averaged over the 12 months ending June 1998. Sim-ilarly, Qw is the 12-month average of the world consumption at the beginning ofthe five-year period.15 For example, in calculating DCNIMP/Qw for the month ofJune 2003 the Qw is the average world consumption for the 12 months endingJune 1998. For comparison purposes, in panel (b) of Figure 7, we also plot thehistorical five-year changes in prices (DP/P) where both DP and P are defined ina similar way to DCNIMP and Qw.

Two features are worth commenting. First, for most of the time duringthis period, the price change implied by growth in China’s net import is between10 and 25 percent depending on the assumed supply elasticity. The mean impliedfive-year price change due to China’s import growth is 11 percent for a supplyelasticity of 0.2 and 22 percent for a supply elasticity of 0.1. In comparison, theaverage historical five-year price change during the same period is 96 percent. Inother words, approximately 11–23 percent of the historical price changes after2002 are attributable to the growth in China’s net import under reasonable esti-mates of crude supply elasticity.

Second, although there is good correlation between the price changesimplied by China’s net oil import and the historical changes in real oil prices,there are important differences.16 For example, when the real oil price spiked in2008 the 12-month moving average of the real oil price in September 2008 was202 percent higher than in September 2003. In contrast, the price increase impliedby China’s net import growth ranges between 12 and 24 percent over this period.Therefore, about 6–12 percent of the price spike in mid-2008 can be attributedto the growth in China’s net import. The result is consistent with our findingsfrom the VAR analysis that China’s oil demand shocks actually lowered the oilprice from its trend in the price spike of 2008.

This result, while suggesting the “China factor” indeed plays an impor-tant role in the crude oil price run-up after 2002, indicates that there are otherimportant factors responsible for the dramatic changes in crude oil price. Ofparticular note, is that the world crude oil production remained largely flat be-tween mid-2005 and early-2008 despite a more than 100% increase in the realoil price. As argued by Hamilton (2009a) and Smith (2009), the failure for crudeproduction to respond to oil price increases appears to have less to do with oildepletion than with restrained investment in some OPEC countries.

5. CONCLUSION

It is often asserted that China’s growing demand for oil is one of themajor reasons for the rapid rise in crude oil prices in the past decade. In this


paper, we make use of monthly data on China’s net oil import from January 1997to June 2010 to assess the relative importance of the “China factor” to the evo-lution of the real oil price during this period. In the first part of our analysis, weexamine the interaction between the growth rate of China’s net oil import and thedeviation of the real oil price from its linear trend under a VAR framework. Wefind that the response of the real oil price to China’s oil demand shocks is smalland statistically insignificant and that only a small fraction of the forecast errorvariance of the real oil price is attributable to China’s oil demand shocks. Fur-thermore, the historical decomposition indicates that the largest positive effect ofChina’s oil demand shock on the deviation of the real oil price from its trendoccurred in 2000. Between 2002 and 2005, no more than five percent of the priceincrease in the real oil price was induced by China’s demand shocks. When theoil price spiked in 2008, China’s demand shocks actually lowered the oil pricefrom its linear trend. In addition, our out-of-sample tests find no evidence forGranger causality between China’s oil demand and the real oil price.

The second part of our analysis calculates the price changes implied byincreases in China’s net oil import from a longer-term supply and demand shiftperspective. Under plausible assumptions of long-term price elasticity of crudeoil supply, approximately 11–23 percent of the historical price changes between2002 and mid-2010 are attributable to the growth in China’s net oil import. Con-sistent with the result from the historical decomposition of the VAR analysis, thecontribution of the “China factor” to the real oil price is even smaller in the pricespike of 2008.

Our analysis casts doubt on the popular view that the demand growthfrom China is the predominant reason for the dramatic oil price increase between2002 and 2008. Notwithstanding, if China’s demand growth continues its trend,it could play a bigger role in the future especially when it is combined with rigidcrude oil supply.

REFERENCES

Ashley, Richard, and Haichun Ye (2010). “On the Granger causality between median inflation andprice dispersion.” Working paper, Department of Economics at Virginia Tech University.

Bai, J. and P. Perron (1998). “Testing for and Estimation of Multiple Structural Changes”, Econo-metrica 66: 47–79.

BBC (2008). “Oil Price May Hit $200 a Barrel”, May 7, 2008, http://news.bbc.co.uk/1/hi/business/7387203.stm (accessed Sep 10, 2010)

Clark, Todd, and Michael McCracken (2001). “Test of Equal Forecast Accuracy and Encompassingfor Nested Models.” Journal of Econometrics 105(1): 85–110.

Clark, Todd, and Kenneth West (2006). “Using Out-of-Sample Mean Squared Prediction Errors toTest the Martingale Difference Hypothesis.” Journal of Econometrics 135(1–2): 155–186.

Clark, Todd, and Kenneth West (2007). “Approximately Normal Tests for Equal Predictive Accuracyin Nested Models.” Journal of Econometrics 138(1): 291–311.

CNN (2004). “China factor driving oil prices”, May 24, 2004, http://edition.cnn.com/2004/BUSI-NESS/05/24/china.oil.demand/index.html (accessed Sep 15, 2010)

Gonclaves, Silvia, and Lutz Killian (2004). “Bootstrapping Autoregressions with Conditional Het-eroskedasticity of Unknown Form.” Journal of Econometrics 123(1): 89–120.


Granger, Clive, and Paul Newbold (1976). “Forecasting Transformed Series.” Journal of the RoyalStatistical Society Series B 38(2): 189–203.

Hamilton, James (2009a). “Understanding Crude Oil Prices.” The Energy Journal 30(2): 179–206.Hamilton, James (2009b) “Causes and Consequences of the Oil Shock of 2007–08” Brookings Papers

on Economic Activity, Spring 2009, 215–261Killian, Lutz (2009). “Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks

in the Crude Oil Market” American Economic Review 99(3): 1053–1069.Krichene, Noureddine (2002) “World Crude Oil and Natural Gas: A Demand and Supply Model.”

Energy Economics, 24(6): 557–576.McCracken, Michael W. (2007). “Asymptotics for Out of Sample Tests of Granger Causality.” Journal

of Econometrics 140(2): 719–752.Smith, James (2009). “World Oil: Market or Mayhem?” Journal of Economic Perspectives, 23(3):

145–164.US Congressional Budget Office (2006). “China’s Growing Demand for Oil and Its Impact of U.S.

Petroleum Markets”. http://www.cbo.gov/ftpdocs/71xx/doc7128/04–07-ChinaOil.pdf (accessedSep 13, 2010)

APPENDIX: RESULT OF A DEMAND SHIFT

D0

P0

D1

P1

S

Q0 Q1

A B

C

Q1’

This graph shows the resulting market equilibrium following a demand shift fromD0 to D1. The movement from A to B reflects this demand shift, although B isnot observable. From B to C represents the movement along the demand curveand from A to C represents the movement along the supply curve. For an observedequilibrium C, the price change from P0 to P1 can be calculated from the quantitychange from Q0 to Q1 and estimated supply elasticity.


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