Effects of coal prices on merchandise prices in China

Mining Science and Technology (China) 21 (2011) 651–654

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Effects of coal prices on merchandise prices in China Ding Zhihua a,⇑, Zhou Meihua a, Liu Yan a,b a b

School of Management, China University of Mining & Technology, Xuzhou 221116, China Finance Department, China University of Mining & Technology, Xuzhou 221116, China

a r t i c l e

i n f o

Article history: Received 5 January 2011 Received in revised form 10 February 2011 Accepted 9 March 2011 Available online 2 November 2011 Keywords: Coal price State space model Merchandise price Price fluctuation

a b s t r a c t Coal is the principal form of energy used in China. Hence, coal price variations are expected to have some influence on merchandise prices. Monthly data from January, 2002, to October, 2010, were used to construct a varying-parameter state space model, and an error correction model, to estimate the influence of coal prices on Chinese merchandise prices. The time lag and the dynamic relationship were determined from the data. A long term equilibrium relationship between coal price and the PPI, and the CPI, can be observed. The long term influence of coal price fluctuations on the PPI is 0.263%. The corresponding value for the CPI is 0.157%. The PPI shows an influence from coal price change in the first period of observation: by eight periods the influence is obvious, after which it diminishes. The effect of coal price change on the CPI is rather weak and has no long term memory. Analysis of variance shows a similar situation. The elasticity coefficient of coal prices on the CPI, or the PPI, fluctuates over the 2002–2004 period. From 2002 to 2007 the influence elasticity on the CPI declined and subsequently levelled off after 2009. Ó 2011 Published by Elsevier B.V. on behalf of China University of Mining & Technology.

1. Introduction The Chinese Consumer Price Index (CPI) peaked in November, 2010, at a 28 month maximum year-on-year increase of 5.1%. The National Development and Reform Commission has recently issued restrictions to coal price increases that affect all large sized coal mining enterprises. Coal is the leading source of China’s energy at this time. Consequently, coal price fluctuations affect various other industries and have an impact on the overall price of merchandise in China. Understanding how coal prices affect merchandise pricing has practical value. The magnitude, and the timing, of the effects are of particular theoretical and practical usefulness. The affect of oil price fluctuations on merchandise prices is, by far, the most extensive area of domestic research in this area. Most studies have found long-term positive correlations between oil prices and merchandise prices [1–4]. Others have quantitatively studied one, or many, nation data and deduced the proportion of merchandise price change due to oil price changes [5,6]. Still others have established models relating oil prices to the national economy [7]. A Computable General Equilibrium analysis of energy prices on specific industries has also been reported [8,9]. However, no reports on the effects on general merchandise prices of a nation have been published using this method. Short term effects of energy price fluctuations on merchandise prices have been discussed [10]. The input–output price model ⇑ Corresponding author. Tel.: +86 13775884258. E-mail address: [email protected] (Z. Ding).

was used to simulate how energy prices will affect general prices for two different cases: Regulated or unregulated energy prices. This shows that at a 6-month lag time the increase in energy cost obviously influences the Producer Price Index (PPI) [11]. However, the impact on the CPI is quite weak at this lag time. Some have even found that a relationship between oil price changes and merchandise price fluctuations is weak or non-existent [12–15]. In summary, both domestic and overseas studies have extensively examined the effect of oil prices on merchandise prices, developing some useful ideas. The research did not highlight the coal centered energy structure of China. Existing studies of Chinese coal prices on merchandise prices are mainly descriptive in nature. This paper describes the magnitude, time lag, and dynamic relationship of the effect coal price fluctuations have on general merchandise prices. 2. Empirical research on the influence coal prices have on the price of Chinese merchandise 2.1. Data processing and tests 2.1.1. Data processing CPI reflects the price variation of commodities and labor of household. It is an important indicator of inflation level. PPI measures the price variation of product manufacturer and is a vital reference for economic policy making and national economy calculation. Since January 2002, the Chinese government has ceased publishing guidance prices for electricity and coal. This has boosted

1674-5264/$ - see front matter Ó 2011 Published by Elsevier B.V. on behalf of China University of Mining & Technology. doi:10.1016/j.mstc.2011.10.008

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the market price of coal. We selected the monthly coal price index (COP), the PPI, and the CPI data over the period from January 2002 to October 2010 available from the China Economic Network Industrial Database and the National Treasury Website. Before processing the data a fixed-base processing approach described in Ref. [11] was used to fix the base ratio at January 2002. Seasonal variation and heteroskedasticity were eliminated from the time series by using the CensusX12 data to make seasonal adjustments. The logarithm of the seasonally adjusted series was taken to get ln(COP), ln(CPI), and ln(PPI), which were used in subsequent analyses. 2.1.2. Unit root test A proper model is required for the unit root test. The time series were plotted and a preliminary judgement concerning whether constant or time dependant tendencies existed was made. The model form was determined and an optimal number of lagged orders was chosen using the Akaike Information Criterion (AIC) principle. Augmented Dickey–Fuller test (ADF) is a testing method to test whether a unit root exists in time series model. The unit root test results (Table 1) show that the original series of three the variables, ln(COP), ln(CPI), and ln(PPI), are all unsteady series. However, after first order differentiation all are steady series. All three series are the same order, single integral series so additional tests of long term equilibrium relationships between the variables could be made. 2.2. Measured magnitude of coal price effects on Chinese merchandise prices 2.2.1. Co-integration test This paper uses the maximum likelihood to conduct co-integration tests on the series. The co-integration test is sensitive to the number of lagged orders in the test equation so a VAR model was first established and the optimal number of lagged orders was determined by the AIC principle. The results indicate that the optimal number of lagged orders in the VAR models constructed from the data is three for both ln(COP) and ln(PPI). Accordingly, the number of lagged orders in the differentiation part of the co-integration equation is two. The results in Table 2 show that, at a significance level of 5%, when H0 = 0 the value of a ktrance test for both models is greater than the 5% critical value. Consequently, the trance test indicates that at the 5% significance level co-integration equations exist. The results of the co-integration tests show there are co-integration relationships between both ln(COP) and ln(CPI) and between ln(COP) and ln(PPI).

p1 X

Dyti þ b2

i¼1

p1 X

ktranceln(COP), ln(CPI) ktranceln(COP), ln(PPI)

H0

H1

ktrance value

Critical value under 5% significance level

r=0 r61 r=0 r61

r>0 r>1 r>0 r>1

31.155 7.112 22.932 5.540

25.872 12.518 18.398 3.841

matrix, a is the adjustment velocity of adjustments to the equilibrium state if the variables deviate from long term equilibrium; b0 is a constant; p is the number of lagged orders; and y signifies either ln(CPI) or ln(PPI); Dyti and Dxti the difference term of CPI, PPI and COP with lag phase i; b1 and b2 are the effect of variables’ shortterm fluctuations on the short-term changes of Dyt; and et reflects the disturbance. Eviews 6.0 was used to establish ecm between ln(COP) and ln(CPI), and between ln(COP) and ln(PPI) in both Eqs. (1) and (2):

DðlnðCPIÞÞ ¼ 0:032  ðlnðCPIÞð1Þ  0:157  lnðCOPÞð1Þ  3:394Þ þ 0:144  DðlnðCPIÞð1ÞÞ þ 0:004  DðlnðCPIÞð2ÞÞ þ 0:042  DðlnðCOPÞð1ÞÞ  0:039  DðlnðCOPÞð2ÞÞ þ 0:002 log L: ¼ 419:290;

AIC ¼ 8:024;

SC ¼ 7:870

DðlnðPPIÞÞ ¼ 0:016  ðlnðPPIÞð1Þ  0:263  lnðCOPÞð1Þ  3:42Þ þ 0:763  DðlnðPPIÞð1ÞÞ  0:0836  DðlnðPPIÞð2ÞÞ  0:006  DðlnðCOPÞð1ÞÞ  0:007  DðlnðCOPÞð2ÞÞ þ 0:001 log L: ¼ 406:578;

AIC ¼ 7:778 SC ¼ 7:625

The testing logic of AIC and SC is to determine the appropriate lagged period length by comparing goodness of fit of different lagged models. The smaller the value of it is the better it is. Eqs. (1) and (2) show that the long term equilibrium equations have coal price coefficients of, respectively, 0.157 and 0.263 for CPI and PPI. This reflects the long term influence of coal price fluctuations on CPI or PPI. The PPI variation elasticity coefficient is greater than that for CPI, as well. The model also gives an adjustment velocity for deviation from long term equilibrium. Model (1) reverses from a non-equilibrium state back to equilibrium at an adjustment velocity of 0.032 when disturbed from long term equilibrium. Model (2) has an adjustment velocity constant of 0.016. 2.3. Measurement of the lag time between coal and Chinese merchandise prices

2.2.2. Construction of an error correction model The vector error correction model (ECM) used is:

Dyt ¼ b0 þ aecmt1 þ b1

Table 2 Co-integration test results: ln(COP) with ln(CPI) and ln(COP) with ln(PPI).

Dxti þ et ðt ¼ 1; 2; . . . ; TÞ

i¼1

ecmt1 ¼ b0 yt1 is the error correction that reflects the long term equilibrium relationship between the variables; the coefficient Table 1 Unit root test results. Statistic

ADF statistics

Critical value under 1% significance level

AIC principle

Conclusions

ln(COP) Dln(COP) ln(CPI) Dln(CPI) ln(PPI) Dln(PPI)

0.5004 3.916 0.637 8.225 1.687 4.364

3.495 3.495 3.494 3.494 3.494 3.494

5.309 5.323 8.802 8.109 7.937 7.929

Unsteady Steady Unsteady Steady Unsteady Steady

2.3.1. Impulse response function The impulse response function describes the dynamic influence in a vector auto-regression model. This effect is from a structured, impacting unit variation to the endogeneous, the current, variables or a lagged response of those same variables within a standard deviation. A series of effects occur after t phases when one variable varies. This is mediated through dynamic links between variables. The impulse response function describes the system dynamic response and allows the lag relationship between variables to be deduced. A second order VAR model was used following the SIMS (1986) adjusted likelihood ratio test. The impulse response function can be seen in Fig. 1. Fig. 1 shows that the CPI has a smaller response to COP than does the PPI. There is no initial response of the CPI but after some time a peak is noted (phase 3). From this point the

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(a) CPI to COP

(b) PPI to COP

Fig. 1. Impulse response function.

Table 3 Contribution of COP variance to the variance of the CPI and PPI. Phase

Variation of CPI (standard deviation)

Contribution of CPI variation by COP (%)

PPI variation (standard deviation)

Contribution of PPI variation by COP (%)

1 2 3 4 5 6 7 8 9 10

0.004361 0.006666 0.008525 0.010153 0.011644 0.013044 0.014382 0.015673 0.016927 0.018154

0.718152 2.194282 2.471790 2.320485 2.046878 1.760058 1.499097 1.276884 1.095821 0.953977

0.004782 0.009215 0.013460 0.017355 0.020856 0.023972 0.026735 0.029184 0.031358 0.033295

8.824849 8.756657 8.838992 8.963748 9.106059 9.255964 9.408464 9.560664 9.710741 9.857493

response drops to zero by phase 9. There is only a small effect from increased coal price on the Chinese CPI and the effect has no long term memory. The response of the PPI to coal price is larger, starting small in the first phase and increasing to its peak eight phases later. The response is gradually diminished after the eighth phase because the coal industry is in the upper part of the Chinese economic supply chain. Coal price increases influence those industries later in the supply chain. The earliest influence involves preliminary processing industries but the lagged time response arises from processing and other industries further along the supply chain.

2.3.2. Variance decomposition Based on the variance decomposition, this paper calculated the contribution rate of coal price fluctuation to CPI and PPI in China, as can be seen in the following table. As contribution of coal price fluctuation to the CPI and PPI in China were calculated from the analysis of variance shown in Table 3. The calculated effect on the CPI is relatively small increasing from 0.72% in phase 1 and increasing to 2.47% in phase 3. Following this peak the effect gradually decreases. The effect on the PPI is relatively larger increasing from the 8.24% value observed at phase 1. This response peaks at 9.86% at phase 9, which indicates that coal price fluctuation is the main cause of PPI variation. 3. Empirical research on the dynamic relationship between coal price and merchandise price: the state space model The state space model is used in metric economics to calculate unobservable time variables, i.e., rational expectancy, measurement error, long term income, or other unobservable factors including measurement equations and equations of state. Eviews 6.0 was used to construct state space models of ln(COP) and ln(CPI) and of ln(COP) and ln(PPI). The model form was error covariance of the measurement equation and the equation of state was O and a Kalman filter. The estimates of the state space models are shown in Table 4. In Table 4 at and bt are the state variables, li, nt, qt, and wt are the respective disturbances from all measurement equations and equations of state.

Table 4 State space models of ln(COP), ln(CPI) and ln(COP), ln(PPI). State space model

Measurement equation

Equation of state

R2

D.W. value

ln(CPI) from ln(COP) ln(PPI) from ln(COP)

lnðCPIÞ ¼ 3; 890; 862 þ at lnðCOPÞ þ lt lnðPPIÞ ¼ 3; 214; 890 þ at lnðCOPÞ þ qt

at ¼ at1 þ nt

0.998 0.996

2.223 2.135

(a) COP and CPI

bt ¼ bt1 þ wt

(b) COP and PPI

Fig. 2. Elasticity coefficient for coal price fluctuations on China’s CPI and PPI.

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Fig. 2 illustrates the monthly elasticity coefficients for coal price fluctuations on China’s CPI and PPI from January, 2002, to October, 2010, calculated by a state space model. As can be seen there the influence of coal price fluctuations on PPI is greater than the influence on CPI. The elasticity of COP on CPI is between 0.1558 and 0.1482, whereas the influence of COP on PPI falls between 0.1783 and 0.1868. The fluctuation of the elasticity to coal price fluctuation on China’s merchandise prices is relatively large, especially from 2002 to 2004. The effect of coal price on CPI and PPI dipped sharply in the early periods but rose later. This is because 2002 was the first year of China’s entry into the WTO with better national economic conditions. Since 2002 the National Development and Reform Commission has had a policy that provides no price guidance for electricity and coal. Fluctuations in coal price have occurred in China since then. In 2008 the influence of coal price on both the PPI and the CPI took on an upward trend and reached a periodic peak due mainly to the over dependence on coal caused by the frequent natural disasters that occurred in China at that time. For example the severe snow period in early 2008 greatly hampered coal transportation and production. From 2008 the influence of coal price fluctuation on the CPI seems to be steady whereas the influence on PPI has taken a downward trend. 4. Conclusions (1) There are positive correlations between coal price and China’s CPI and PPI over the long term. The influence on PPI is rather obvious. When short-term fluctuations occur that move prices away from the long-term equilibrium the models for CPI and PPI adjust at a rate of 0.03 and 0.016 as they move back to the state of equilibrium. (2) The impact response of coal price on CPI is relatively small and does not have a long term memory. The impulse response of PPI from coal price within one standard deviation is rather obvious. Although an increase in coal price influences the PPI in the first period the influence is relatively small (0.142%). After a lag of eight periods a more obvious influence on PPI occurs with a peak at 0.377%. Similar conclusions may be drawn from the results of variance decomposition. (3) We conclude from the dynamic relationship empirical study that the elasticity coefficient of coal price influence on the CPI and the PPI fluctuated during each month over the

2002–2004 time period. The influence of coal prices on CPI declined during each month during from 2002 to 2007 having a nadir at the end of 2006 and the beginning of 2007. Subsequent to 2008 the influence of coal prices on the CPI rises steadily and then levels off.

Acknowledgments Financial support for this work, provided by the National Natural Science Foundation of China (No. 71003097), Jiangsu Province Social Science Foundation (No. 10EYD025) and 2008 China University of Mining and Technology Youth Foundation Program (No. 2008W04), is gratefully acknowledged. References [1] Gracia C. Oil prices, economic activity and inflation: evidence for some Asian Countries. Q Rev Econ Finance 2005;45(1):65–83. [2] Cologni M. Oil prices, inflation and interest rates in a structural cointegrated VAR model for the G-7 countries. Energy Econ 2008;30(3):856–88. [3] Yang T, Nie R, Liu Y. Simulation and analysis of price conduction between coal and electricity based on the price conduction complex networks. China Min Mag 2009;9:31–5 (in Chinese). [4] Chang YH, Jiang C. Oil price fluctuations and Chinese economy. Energy Policy 2003;11:1151–65. [5] Li L. Research on effect of oil price fluctuation on China economy. Petrol Univ J 1993;3:92–6 (in Chinese). [6] Chen SS. Oil price pass through into inflation. Energy Econ 2008;31(1):126–33. [7] Yu B, Chi CH, Su GF. Measurement model of effect of oil price on national economy. Quant Econ Tech Econ Res 2002;5:74–6 (in Chinese). [8] Liu Q. Model research of effect of oil price variation on China national economy. Quant Econ Tech Econ Res 2005;3:16–27 (in Chinese). [9] Han MC, Fan Q. Research on the relationship between international oil price fluctuation and industrial ready-made product export in China. Quant Econ Tech Econ Res 2007;2:64–72 (in Chinese). [10] Valerie M, Sandrine L. Oil price and economic activity: an asymmetric cointegration approach. Energy Econ 2008;30:847–55. [11] Lin BQ. Effect of energy price increase on general price increase of China. Econ Res 2009;12:66–79 (in Chinese). [12] Hooker M. What happened to the oil price macroeconomy relationship? J Monetary Econ 1996;38(8):195–213. [13] Berument. Inflationary effect of crude oil prices in Turkey. Stat Mech Appl 2002;316(4):568–80. [14] Paul N, Leiby W. Oil price shocks and the macroeconomy: what has been learned since 1996? Energy J 2004;25(2):1–32. [15] Li C, Wang B, Ma W. Relationship between international oil price and inflation cycle fluctuation. Stat Res 2010;4:28–36 (in Chinese).