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Abstract

  1. Top of page
  2. Abstract
  3. 1. introduction
  4. 2. main transmission mechanisms of an oil price increase
  5. 3. factor-augmented vector autoregressive methodology analysis
  6. 4. conclusion
  7. references

Abstract.  The aim of this paper is to investigate the impacts of oil prices on the Chinese economy. To this end, we rely on the factor-augmented vector autoregressive methodology, which allows us to evaluate the response of various macroeconomic variables to an oil price shock. Our results suggest that an oil price shock leads to: (i) a contemporaneous increase in consumer and producer price indexes, inducing a rise in interest rates; (ii) a delayed negative impact on GDP, investment and consumption; and (iii) a postponed increase in coal and power prices.


1. introduction

  1. Top of page
  2. Abstract
  3. 1. introduction
  4. 2. main transmission mechanisms of an oil price increase
  5. 3. factor-augmented vector autoregressive methodology analysis
  6. 4. conclusion
  7. references

With a population of more than 1.3 billion and an economic growth rate of almost 12% in 2007, China has become a major player in the world economy. This crucial role is particularly perceptible in world energy markets. Indeed, while being a self-sufficient energy consumer up to 1993, China is now the second largest importer of energy products in the world, behind the USA. This huge growing demand for energy is the result of China's spectacular economic growth, and mainly comes from two sectors: industry (including construction) and transportation. For industry, the increasing demand for energy is expected to continue, because the Chinese economic growth is highly industry-oriented and energy-intensive. Considering the second of these sectors, highway and jet traffic have experienced rapid expansion since the 1990s, together with rising private automobile ownership.1 Because petroleum is the dominant fuel for transport, the development of the Chinese transportation sector has led to a surge in demand for oil products. As noted by Skeer and Wang (2007), due to the sharp increase of fuel use in the transportation sector, there will be large increases in China's crude oil and petroleum product imports. Within this context of high dependence on oil, the aim of the present paper is to investigate the impacts of oil prices on the Chinese economy.

The Chinese oil price system deserves some attention. Indeed, prices of oil products are lower in the domestic market than in the international market. Concerning crude oil producer prices, four stages characterize their evolution. In the first stage, ending in 1981, oil prices were under tight state control. During the second stage, starting in 1981 and ending in 1994, the two-tiered pricing system was introduced, the two price tiers being in quota prices and market prices once the quota assigned by the state had been met. From 1994 to 1998, defining the third stage, price controls were re-imposed. Finally, since 1998, the fourth stage is such that domestic crude oil prices are set in accordance with the world market price.2 Nonetheless, consumer prices for petroleum products are still regulated and the price fixed by Chinese authorities lies well below international prices,3 a fact that accentuates the growing demand for oil in China. Consequently, oil companies and refiners in particular are bearing the costs of the subsidization. This pricing system might explain why the Chinese economy does not seem to be highly sensitive to the international oil price. Nevertheless, such a situation leads to serious disturbances. Refiners are induced to export rather than to supply the domestic market, inducing shortages and leading oil firms to put pressure on the government to raise domestic prices, to ease taxes or to guaranty a year-end pay-off. As a consequence, oil price increases in international markets might have an impact on the Chinese economy. On one hand, they have been partly passed through to consumer prices since 1981; on the other hand, the cost of the subsidies is shared by oil companies and the government, which leads to less investment in energy infrastructure and less of the budget being available for growth-promoting spending, as well as tensions about ‘who should pay’.

Given these specificities concerning China, our purpose is to investigate the impact of oil price changes on the Chinese economy. More specifically, we proceed to a multivariate analysis to put forward the mechanisms of propagation of an oil price change. In this context, a standard approach relies on vector autoregressive (VAR) modelling. However, Bernanke et al. (2005) highlight several drawbacks of this approach, including the degrees of freedom issue: to conserve degrees of freedom, standard VAR modelling generally includes a small number of variables, which is unlikely to account for all the relevant information. To circumvent this problem, we follow Bernanke et al. (2005), who proposed an alternative approach combining the standard VAR analysis with factor analysis. The underlying idea of the resulting factor-augmented VAR (FAVAR) models is that a large amount of information about the economy can be summarized using a relatively small number of factors that stand in for broad concepts like ‘economic activity’ or ‘credit conditions’. Augmenting usual VAR models with a small number of such estimated factors that summarize a broad range of economic variables enables us to exploit more information and to obtain the responses of a large set of variables to exogenous innovations. Here, we consider the following set of variables: GDP, consumer price index (CPI), producer price index, households’ consumption expenditure, investment, imports, exports, interest rates, money supply and unemployment rate. We also include in our set of variables producer prices for electricity and coal because: (i) the Chinese energy market is unbalanced and clearly dominated by coal; and (ii) electricity use tends to increase in developing provinces, following the changes in income distribution patterns.

The rest of the paper is organized as follows. Section 2 reviews the main theoretical mechanisms through which an oil price increase may affect the economic system. Section 3 is devoted to the FAVAR modelling and investigates the propagation mechanisms of an oil price increase on the Chinese economy. Section 4 concludes.

2. main transmission mechanisms of an oil price increase

  1. Top of page
  2. Abstract
  3. 1. introduction
  4. 2. main transmission mechanisms of an oil price increase
  5. 3. factor-augmented vector autoregressive methodology analysis
  6. 4. conclusion
  7. references

As noted by Lescaroux and Mignon (2008), among others, despite the substantial research on the impacts of oil prices on economic activity, we are still far from reaching a consensus regarding the transmission channels of an oil price increase to the economic system.4 Moreover, the way oil prices influence the economy and the magnitudes of their effects may have evolved through time. Indeed, the mechanisms that were at work during the first two shocks are not necessarily the same today, i.e. since the beginning of the 2000s.5

Globally, the 1970s were characterized by an increasing dependence of the economies on oil, large and unprecedented movements in the oil market and poor macroeconomic context, especially in the USA. In this configuration, it was natural to investigate the potential links between oil prices and macroeconomic activity. Initial studies aimed to elucidate the new oil shock phenomenon by exploring its effects from the demand side: a hike in the crude price was taken as an exogenous inflationary shock (Pierce and Enzler 1974) or as a transfer of wealth from importing to exporting countries, through a shift in the terms of trade (Hickman et al. 1987). This shift, in turn, depends on energy-import intensity. The bigger the intensity, the bigger the macroeconomic impact. By and large, these analyses identified the following consequences: a slowdown in domestic demand (hence, lower GDP and higher unemployment) and inflationary pressure (a risk of tighter monetary policy).

An increase in the oil price also affects supply (Rasche and Tatom 1977a,b,c), because energy is one of the basic inputs in the production process. As a consequence, a rise in the cost of energy can be interpreted as a reduced availability of a basic input to production, leading to an increase in the cost of production. This leads to a decline in output growth and productivity. The real business cycle theory developed in the 1980s reinforced the interpretation whereby oil shocks were supply shocks, and most subsequent studies were undertaken within this theoretical framework.

Given these two side effects, let us provide further details about the transmission channels through which oil prices influence the economy. As previously mentioned, an oil price increase can be viewed as an inflationary shock. As a consequence, it leads to a rise in the CPI, depending upon the share of oil products in the consumption basket. In addition to this direct effect, there are also second-round effects. As a result of the decline in their purchasing power, households may ask for increasing wages, leading to price–wage loops. Turning to the firms, they can pass the oil price increase on to selling prices. These effects tend to feed a wage–price spiral and to generate upward revisions of inflation expectations. The reaction of consumer prices and inflation to oil price movements has been investigated by many authors, including Hooker (2002), Barsky and Kilian (2004) and LeBlanc and Chinn (2004). While Barsky and Kilian (2004) show that oil price increases generate high inflation, LeBlanc and Chinn (2004) argue that oil prices have only a moderate impact on inflation.

An oil price increase might also have a negative impact on consumption, investment and employment. Consumption is affected through its positive relation with disposable income, and investment by increasing firms’ costs and, possibly, by increasing uncertainty, which leads to a postponement of investment decisions (Ferderer 1996). Considering households, an oil price increase generates a rise in domestic fuel prices, leading to a decrease of their purchasing power and slowing their consumption expenditures. However, this effect can be tempered if consumers expect the rise in oil prices to be transitory. In this case, they will attempt to smooth their consumption by saving less or borrowing more, pushing up real interest rates. Turning to employment, if the oil price increase is long lasting, it may lead to a change in the production structure and have a deep impact on unemployment. Indeed, a rise in oil prices diminishes the return of sectors that are oil-intensive and can incite firms to adopt and construct new production methods that are less intensive in oil inputs. This generates capital and labour reallocations across sectors that may affect unemployment (Loungani 1986). In this context, Caruth et al. (1998) investigate the impact of oil price movements on the labour market and Davis and Haltiwanger (2001) focus on the influence of oil price dynamics on job creation and destruction. The impact of oil price movements on the labour market can differ according to the considered horizon. As shown by Keane and Prasad (1996), oil price increases tend to reduce employment in the short run, but to increase employment in the long run. This reverse relationship in the long run might be a result of the complementarities and substitutabilities across different segments of the labour market.

On the whole, various transmission channels exist through which oil prices may affect economic activity. Turning now to the empirical studies, it should be noted that, to the best of our knowledge, little has been done concerning the links between energy prices and economic activity in China. The published empirical literature mainly investigates two topics. A first strand of the literature relies on the determinants of energy consumption (Chan and Lee 1996Wei 2002Cornillie and Fankhauser 2004Han et al. 2004Shiu and Lam 2004Wolde-Rufael 2004) and the explanations of the energy intensity fall observed in China since the 1990s (Smil 1990Kambara 1992Sinton and Levine 1994Lin and Polenske 1995Garbaccio et al. 1999Zhang 2003Fisher-Vanden et al. 2004). The principal study on energy prices is due to Hang and Tu (2007), who show that energy prices play a key role in explaining the energy intensity fall in China.

A second strand of the empirical literature is devoted to the forecasts of growth in China's energy consumption.6 This is an important issue because the upward trend of energy consumption in China increasingly affects global energy markets, especially oil and natural gas. Moreover, as pointed out by Crompton and Wu (2005), providing long-term forecasts allows evaluation of the need for future trade and investment strategies to ensure the security of China's energy supply.7

3. factor-augmented vector autoregressive methodology analysis

  1. Top of page
  2. Abstract
  3. 1. introduction
  4. 2. main transmission mechanisms of an oil price increase
  5. 3. factor-augmented vector autoregressive methodology analysis
  6. 4. conclusion
  7. references

We use FAVAR modelling to investigate the influence of international oil prices on the Chinese economy. We first present the framework, before detailing the model construction and empirical results.

3.1. Framework

Standard VAR modelling suffers from four main drawbacks (see Bernanke et al. 2005, among others). First, it requires a plausible identification of shocks and many authors have disagreed about the appropriate strategy for identifying those shocks (Bernanke and Mihov 1998Christiano et al. 2000). Second, to conserve degrees of freedom, standard VAR are generally low-dimensional in the sense that they include a small number of variables. This small number of variables is unlikely to account for all the information representative of the Chinese economy. Third, because the number of variables is limited, the considered variables must precisely reflect their theoretical counterparts. As illustrated by Bernanke et al. (2005), for instance, the concept of ‘economic activity’ may not be exactly represented by GDP or industrial production or any other observed variable. Moreover, many observed variables are subject to revisions and are never free of measurement errors. Fourth, impulse responses issued from standard VAR modelling can be observed only for the included variables, which represent only a small fraction of variables that the researcher or policy-maker cares about. To circumvent these problems, we follow Bernanke et al. (2005) by considering an approach that combines the standard VAR analysis with factor analysis, and proceed to the estimation of FAVAR models.8 The underlying idea is that a large amount of information about the economy can be summarized by a relatively small number of factors that stand in for broad economic concepts. As a consequence, to solve the degrees of freedom issue in standard VAR models, the usual VAR can be augmented with estimated factors. This enables us to exploit more information and to obtain the responses of a large set of variables to exogenous innovations.

Moreover, the ability of FAVAR models to deal, at least partly, with measurement errors is particularly beneficial for our study on China considering the lack of accuracy of the series: Chinese statistics are frequently criticized for their poor quality.9 Besides, given the unavailability of quarterly data for some indicators, we have to use interpolated series (see infra).

Following Bernanke et al. (2005), the general form of the FAVAR model can be stated as follows:

  • image(1)

and

  • Xt =ΛfFt +ΛyYt + ut,(2)

where Yt is an M × 1 vector of the observed macroeconomic variables, including oil prices, Xt is an N × 1 vector of informational time series (with N large), Ft is a K × 1 vector of unobserved factors (with K being small) that summarize the information in Xt, ɛt is the error term with mean zero. Φ(L) is a lag polynomial of order d, Λf denotes an N × K matrix of factor loadings, Λy is an N × M matrix and ut is an N × 1 vector of error terms supposed to be mean zero and uncorrelated (or weakly correlated).

The unobserved factors, Ft, might be seen as diffuse concepts, such as ‘economic activity’ or ‘price level’ that cannot be perfectly represented by one or two series but rather emerge in a wide set of economic variables. Because these Ft factors are not observable, equation 1 cannot be estimated directly. This is why a second equation is added: equation 2 supposes that the unobserved factors and the observed variables, Yt, are linked to various economic time series that are grouped into Xt, the vector of informational time series. Therefore, this equation states that both vectors Ft and Yt are common forces that drive the dynamics of Xt (see Bernanke et al. 2005).

If all the variables are assumed to be perfectly observed, then Yt includes all these variables and Ft equals the null set. In this case, equation 2 is useless and the FAVAR model reduces to a standard VAR model. However, as previously mentioned, perfect observation is a strong assumption and, as a consequence, the FAVAR model is more realistic than the VAR one. As argued by Bernanke et al. (2005) among others, one might consider that almost all macroeconomic variables are not directly observed for two main reasons: existence of measurement errors (due to multiple rounds of revisions) and imperfect correspondence between theoretical concepts and specific time series. These arguments provide justifications for assuming that all macroeconomic variables, except the variable of interest, may be considered as unobservable. In this case, the vector Ft includes those macroeconomic variables and Yt only contains the variable of interest.

The FAVAR model can be estimated in two ways. The first method, proposed by Stock and Watson (2002), relies on a two-step principal components approach and the second is based on a single step Bayesian likelihood method. As noticed by Bernanke et al. (2005), it is not clear which method dominates the other. We retain here the principal components method because it has the advantage of computational simplicity compared to the other procedure. In the first step, we use equation 2 to estimate the vector t of unobserved factors Ft using the principal components analysis (i.e. the first K principal components of Xt). In the second step, we estimate the FAVAR model (eqn 1) using standard methods and replacing Ft by t.10

3.2. Selection of variables and model construction

We consider quarterly data for China over the 1980–2006 period. To summarize Chinese economic activity, we rely on the following macroeconomic variables: GDP, CPI, producer price index (PPI), households’ consumption, investment, imports, exports, 1-year interest rates (lending and deposit), money supply (M2) and unemployment rate. These variables are usual indicators for the business cycle. Notably, they cover the main transmission channels reviewed previously (see Section 2). They were also chosen for practical considerations in the sense that they were available for our considered period.11 Data concerning price indexes, imports, exports, interest rates and money supply are extracted from the IMF International Financial Statistics. All other data are taken from the World Bank World Development Indicators. All these data are seasonally adjusted.

Turning to energy prices, we consider the crude oil price on the international market but also two other series: the PPI for coal and for electricity. Despite the regulation of energy prices in China, our variable of interest is the price of oil on international markets. Indeed, our purpose is to test whether oil prices affect the Chinese economy in spite of the state control. As explained before, this might be the case because: (i) oil price increases in international markets are partly passed through to consumer prices, most of the time, in our sample; (ii) the financial resources of oil firms and the government devoted to the subsidization of energy products could be used more efficiently; and (iii) the regulation leads to serious dysfunction (notably shortages). In relation to the first point, it seems interesting to consider PPI for other energy products as well, in order to analyze the transmission of an oil price change. We focus on coal and power due to the specific energy situation of China. Indeed, China's energy balance is dominated by coal, despite the move towards oil products, whereas demand for power is rapidly growing for both industrial and residential usages. Annual price data series were extracted from the China Statistical Yearbook12 and have been converted into quarterly frequency using cubic spline interpolation. It would have been desirable to include consumer prices too, but, unfortunately, to the best of our knowledge, such series are not available13 (retail prices for gasoline can be found from 1994, which constitutes too short a sample).

We consider series in real terms to remove the specific impact of inflation that may cause spurious common behaviour among series. Consequently, all macroeconomic time series but interest rates, price indexes and unemployment rate are expressed in constant RMB. Interest rates and energy price indexes are expressed in real terms, using the CPI as a deflator. All series except interest and unemployment rates have been transformed in logarithm.

We first proceed to the application of standard unit root tests (Dickey and Fuller 1979, 1981; Phillips and Perron 1988; Kwiatkowski et al. 1992) on real series. The first two tests are based on the null hypothesis of a unit root, while the Kwiatkowski, Phillips, Schmidt and Shin test considers the null of no unit root. With some exceptions, all our considered series appear to be integrated of order one, which is a standard result in the published literature for such series.14

To pursue the preliminary analysis, we perform bivariate Granger causality tests as well as examine the interactions in our set of variables.15 Three main conclusions emerge. First, international oil prices can be considered as exogenous between 1980 and 2006. Considering the growing share of China in global oil consumption, this result should not last for long: the causality from GDP to oil prices becomes marginally significant at the 10% level, which is consistent with the conclusion of Zhao and Wu (2007, p. 4245), who argue that China is ‘a large country in the international market and its trade behavior thus can influence the international price’. Second, PPI for energy are influenced both by international oil prices (as expected, considering the leading role played by oil prices on the international energy markets) and by national macroeconomic variables. Third, many causal relationships exist from energy prices to macroeconomic variables. On the whole, the energy price series that causes the majority of macroeconomic series is the coal one, confirming the importance of coal in the Chinese economy.

Turning now to the FAVAR construction and following the arguments by Bernanke et al. (2005) concerning the choice between observable and unobservable variables, only the international crude oil price is included in the vector Yt. The vector Xt includes the following variables: GDP, CPI, PPI, households’ consumption, investment, imports, exports, lending rate, deposit rate, M2, unemployment rate and producer price indexes for coal and electricity. Furthermore, we include the GDP of OECD countries16 as an exogenous variable in the FAVAR model to account for the potential impact of the rest of the world on the Chinese economy. Indeed, the Chinese industry is highly oriented towards exportation and it would be important to not neglect the influence of foreign economic activity. All the series were considered in real terms and have been transformed to induce stationarity.

3.3. Empirical implementation and results

When estimating a FAVAR model, the length of the autoregressive structure and the number of factors included in the VAR have to be determined. Following Bernanke et al. (2005), we checked the empirical plausibility of a large variety of specifications to select our reference model. Note that our choice of the autoregressive structure has been guided by usual information criteria. Following Hamilton and Herrera (2004), who show that autoregressive models with fewer than eight lags for quarterly data are unable to correctly account for the oil price influence, we rejected such parsimonious autoregressive structures and selected the model that minimizes Akaike and Schwarz information criteria among specifications with eight lags or more. The most successful specification appears to be the VAR(8) with five factors, including contemporaneous and eight lagged values for the OECD GDP.17

As noted by Bernanke et al. (2005), the factors estimated by principal components might be shown to stand in for broad concepts like ‘economic activity’, ‘price level’ or ‘credit conditions’. The matrix of eigenvectors that reports the linear relationships between the principal components and the informational variables enables us to interpret the factors. Table 1 shows the principal components decomposition of our group of variables.18

Table 1. Principal components analysis
 Component 1Component 2Component 3Component 4Component 5Component 6Component 7
Eigenvalue3.632.381.7 1.440.990.890.77
Variance proportion0.280.180.130.110.080.070.06
Cumulative proportion0.280.460.590.7 0.780.850.91
Eigenvectors
VariableFactor 1Factor 2Factor 3Factor 4Factor 5Factor 6Factor 7
  1. CPI, consumer price index; PPI, producer price index.

GDP0.46–0.08–0.06–0.14–0.03–0.060.12
Investment0.4–0.310.12–0.2–0.02–0.040.16
Consumption0.33–0.17–0.33–0.33–0.22–0.260.02
Unemployment0.010.250.48–0.020.430.020.5
M20.02–0.21–0.01–0.210.75–0.34–0.46
Exports0.01–0.130.010.68–0.1–0.27–0.32
Imports0.16–0.2–0.110.460.14–0.430.52
Lending rate–0.43–0.120.1–0.22–0.19–0.40.12
Deposit rate–0.42–0.110.12–0.22–0.17–0.410.12
CPI0.230.510.13–0.02–0.11–0.33–0.14
PPI0.230.420.33–0.04–0.19–0.31–0.21
PPI for power0.09–0.350.520.06–0.050.12–0.16
PPI for coal0.1–0.360.46–0.03–0.240.05–0.09

Basically, the first factor could be seen as an indicator of ‘real economic activity’ in a broad sense. The second factor might reflect essentially the ‘overall price level’. The third one seems to express mainly the ‘relative producer price level for other energy resources’ (as well as unemployment, surprisingly). The fourth factor is related to ‘trade activity’ and the fifth one may reflect ‘unanticipated changes in money supply’ (through its strong positive correlation with M2 and its negative correlation with real activity and price measures). The remaining factors are more difficult to explain. Of course, these interpretations are simplistic and subject to debate.

To investigate the mechanisms of propagation of an oil price increase through the Chinese economy, we evaluate generalized impulse-response functions (Pesaran and Shin 1998) for the estimates of the unobserved factors. The results are reported in Figure 1. Overall, oil price increases negatively affect real economic activity and exert an upward pressure on overall prices first, and then on relative producer prices for other energy resources. Despite the regulation of consumer prices for energy resources, international oil prices do affect the Chinese economy. As mentioned earlier, the influence might be indirect, through pernicious effects of shortages or through inefficient use of resources by oil firms and the government.

image

Figure 1. Generalized impulse-response functions of the unobserved factors to an oil price shock (one standard-error confidence bounds generated from FAVAR with five factors)

Download figure to PowerPoint

To assess more precisely the effects of an oil price increase on the Chinese economy, the impulse responses of the unobserved factors can be used to draw out the dynamic responses of any series among the informational variables. More specifically, the response of a variable Xito a shock in the oil price is given by:

  • image

where ∂Fkt+j/Yt is the j-period ahead response of estimated factor k (k = 1, . . . , K) to a shock in the international oil price. The weights λi, i = 1, . . . , N, are calculated using series standard deviations and eigenvectors given by the principal components analysis.

Figure 2 reports the results for the more interesting variables. Some appealing conclusions emerge. A positive oil price shock has a direct, short-lived, impact on PPI and CPI, which rise at first. This induces a monetary response: after a brief drop reflecting the rise in inflation, real interest rates go up (both the reported lending rate and the unreported deposit rate). Thus, the pressure on overall prices fades away quickly, whereas demand slows. The effects on GDP, investment and consumption are strongest in the second year after the shock.19 In contrast, unemployment is not significantly influenced by oil prices. This might be related to the fact that official unemployment figures are well-known for being misleading, and notably for not capturing the severe underemployment in China. Imports and exports are not significantly affected as well, but the estimated impulse response coefficients suggest an improvement of the trade balance due to a slowdown in imports. Relative prices for power and coal increase. Their responses become significant in the third year following the oil price shock. This delayed impact reflects the natural transmission of an oil price variation to prices of other energy resources, as the substitution possibilities are limited in the very short run, but it presumably results from the regulation of energy prices as well, which acts against market forces.

image

Figure 2. Generalized impulse-response functions to an oil price shock (one standard-error confidence bounds generated from FAVAR with five factors)

Download figure to PowerPoint

As robustness checks, we finally perform other FAVAR estimations by varying the number of autoregressive lags and the number of included factors. Results were found to be rather insensitive to the choice of the length of the autoregressive structure, as long as it was not too short (typically, longer than four). Turning to the selected factors, the qualitative nature of the results was basically unaffected by changing the number of factors, but this leads to less significant responses, especially when the number of factors increases.

4. conclusion

  1. Top of page
  2. Abstract
  3. 1. introduction
  4. 2. main transmission mechanisms of an oil price increase
  5. 3. factor-augmented vector autoregressive methodology analysis
  6. 4. conclusion
  7. references

The aim of this paper was to investigate the impact of oil prices on the Chinese economy. To this end, we estimated a FAVAR model. This methodology has the advantage of circumventing some drawbacks of the standard VAR approach: by combining the usual VAR approach with factor analysis, we are able to consider a larger set of variables and to deal with measurement errors. Estimating a quarterly FAVAR over the 1980–2006 period, we show that an oil price shocks leads to: (i) a contemporaneous short-lived increase in PPI and CPI, inducing a rise in interest rates; (ii) a delayed negative impact on GDP, investment and consumption; and (iii) a postponed increase in coal and power prices, reflecting the natural transmission of the oil price shock to other energy resources, together with the limited substitution possibilities in the very short run.

Footnotes
  • 1

    For a detailed study of the Chinese transportation sector, see Skeer and Wang (2007).

  • 2

     For a description of the price system deregulation for coal and electricity, see Hang and Tu (2007).

  • 3

     For example, on April 22 2008, the price per litre of regular unleaded gasoline was $US0.61 in Beijing according to Reuters.

  • 4
  • 5

    On the differences between the first two shocks and the 2000s oil price increase, see Blanchard and Gali (2007) and Lescaroux and Mignon (2008).

  • 6

    Nel and Cooper (2008) provide a critical review of the International Energy Agency's oil demand projections for China.

  • 7

     For studies on these forecasts, one may refer to Chan and Lee (1996), Zhou (1999), Keii (2000), Han et al. (2000) and Hirschhausen and Andres (2000).

  • 8

    Note that Forni et al. (2003) have also proposed a factor model. Their framework differs from Bernanke et al. (2005) in that it is based on a structural approach, the common factors being identified as structural shocks using long-run restrictions.

  • 9

    In relation to China's energy statistics, see Fisher-Vanden et al. (2004) and IEA (2007).

  • 10
  • 11

    Note that some series were available only at an annual frequency: households’ consumption, investment, unemployment rate and GDP before 1999. They have been converted into quarterly frequency using cubic spline interpolation.

  • 12
  • 13

    This is all the more regrettable, as the lack of detailed information about final prices prevents us from evaluating the wedge between domestic and international prices and the cost of the price control.

  • 14

    Results are not reported here but are available from the authors upon request.

  • 15

    Of course, all the series have been transformed to induce stationarity, meaning that they have been log first-differenced (interest and unemployment rates have only been first differenced); results are not reported to save space but are available from the authors upon request.

  • 16

    The OECD GDP is taken from the OECD database.

  • 17

    Note that our choice of eight lags is in accordance with the VAR models that aim to estimate the oil price–macroeconomy relationship with quarterly data. It should also be mentioned that, given the various variables considered and the lag structure, we have to estimate a lot of parameters relative to the number of observations.

  • 18

    To save space, we only report the first seven factors.

  • 19

    Note that the impulse response analysis enables us to calculate cumulative oil price GDP elasticities, which are the sum of the impulse response coefficients of GDP over several quarters for an oil price shock at a specific time. The cumulative oil price GDP elasticity is strongest when it is evaluated over 10 quarters and its value is –0.58%. The order of magnitude of this estimate seems plausible; it is much lower than the value of –2% reported by the IEA (2004), but appears to better reflect what has happened over the past 5 years.

references

  1. Top of page
  2. Abstract
  3. 1. introduction
  4. 2. main transmission mechanisms of an oil price increase
  5. 3. factor-augmented vector autoregressive methodology analysis
  6. 4. conclusion
  7. references
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