Time varying macroeconomic effects of energy price shocks: A new measure for China

doi:10.1016/j.eneco.2018.05.014

Energy Economics

Volume 73, June 2018, Pages 146-160

https://doi.org/10.1016/j.eneco.2018.05.014 Get rights and content

Highlights

•
We develop a new index of quarterly energy prices of China
•
A class of time-varying BVARs is used to examine the effects of energy price shocks on China's macroeconomy
•
Energy price shocks are found to generate statistically significant reductions in real GDP growth and increases in inflation.
•
The interest rate responses suggest the PBOC prioritises inflation stabilization over output growth.

Abstract

In this paper, we examine the effects of world energy price shocks on China's macroeconomy over the past two decades. We begin by showing that the use of oil prices as a proxy for more general energy price dynamics is not appropriate for the case of China. Having established this fact, we propose a new energy price index which accurately reflects the structure of China's energy expenditure shares, and intertemporal fluctuations in international energy prices. We then employ a sufficiently rich set of time varying VARs, identified through a new set of agnostic sign restrictions, to estimate the effects of energy price shocks on China's macroeconomy. Our main result is that positive energy price shocks generate statistically significant reductions in real GDP growth and increases in inflation. Interestingly, both the sets of responses have consistently declined over the sample period. Next, the interest rate responses are found to be consistently positive over the sample period. Given the aforementioned stagflation result, this suggests that the PBOC is more focused on inflation stabilization as compared to facilitating output growth. All presented results are shown to be robust under both official national data and those developed by Chang et al. (2015), thus strengthening our conclusion that energy price shocks have significant time varying effects on China's macroeconomy.

Introduction

The emergence of China as a major economic player and energy consumer has generated interest on the relationship between energy price shocks and China's macroeconomy (Tang et al., 2010, Du et al., 2010, Cunado et al., 2015, Wei and Guo, 2016, Herwartz and Plödt, 2016, Cross and Nguyen, 2017). Consistent with existing literature in the US and other developed economies, the standard approach to modeling energy price shocks has been to examine the effects of an exogenous, unanticipated rise in the price of imported crude oil prices (see, e.g., Hamilton, 1983, Kilian, 2008, Kilian, 2009, June, Kilian, 2014; Peersman and Van Robays, 2009, Lippi and Nobili, 2012 or Baumeister and Peersman, 2013b). While an examination of the relationship between oil price shocks and China's macroeconomic variables is of interest in its own right, a deeper investigation into the structure of China's quarterly energy expenditure shares reveals that the use of oil prices as a proxy for modeling more general energy price shocks paints an incomplete picture. After establishing this fact, the objective of this paper is to propose a new index of quarterly energy prices, which accurately reflects both the structure of China's total energy expenditure shares on primary commodities, and intertemporal fluctuations in international energy prices. Once established, the index is then used alongside three key macroeconomic variables; inflation, real GDP growth and a short term interest rate, to investigate the effects of energy price shocks on China's macroeconomy over the past two decades.

To provide evidence in support of the claim that oil prices are not a good proxy for more general energy price dynamics, Fig. 1 compares the expenditure shares on primary energy commodities: coal, crude oil and natural gas, within China and the US, over the period 1993Q1–2016Q2.¹

Two points are worth emphasizing. In the first instance, while oil expenditure is clearly the dominant share of total energy expenditure in the US, the data reveals that coal, not oil, is the major source of energy expenditure in China. More precisely, the total expenditure on oil in the US contributed around 70% of the primary commodity energy expenditure share, with coal and natural gas contributing the remaining 30%. In contrast, oil expenditure in China's economy accounts for just 34% of the total energy expenditure share, with coal contributing to 62% and natural gas the remaining 4%. In addition to this fundamental difference in average expenditure shares, we also highlight the fact that while the primary energy expenditure shares in the US are relatively stable, China's expenditure shares have significantly changed over time. More precisely, the total energy expenditure on oil in 1993 was just 24%, compared to 35% in 2016. Similarly, the total energy expenditure on natural gas has grown from just 2% in 1993 to 8% in 2016.

Combined together, these simple empirical facts suggest that the common use of oil prices as a proxy for more general energy price dynamics in studies on the US economy, does not extend to the case of China. Instead, since it comprises the highest average expenditure share, a superior proxy is provided by coal. That being said, the second empirical observation suggests that a superior proxy to using coal prices can be obtained by an energy price index that accurately reflects both the structure of China's energy expenditure shares along with fluctuations in international energy prices. For this reason, the first objective of this paper is therefore to develop such an index. Once established, we then investigate the effects of energy price shocks on China's macroeconomy over the past two decades.

To this end, our empirical analysis employs a sufficiently rich set of time varying VAR models: a traditional constant parameter VAR, a time varying parameter VAR, a constant parameter VAR with stochastic volatility and a fully flexible time varying VAR with stochastic volatility. The motivation for this set of models stems from the recent work of Cross and Nguyen (2017), who show that when modeling the relationship between China's economic growth and global oil price shocks, a time-varying parameter VAR with stochastic volatility provides superior in-sample fit as compared to its time-invariant counterparts.² To elicit a distinction between any sources of time variation within the endogenous relationship between energy prices and China's macroeconomy and any time varying volatility in any of the innovations, we conduct a formal Bayesian model comparison exercise through which the relevant features of the data are identified. After selecting the best model, the associated structural VAR (SVAR) is identified through the use of a new set of agnostic sign restrictions, which are motivated by structural differences between China and the US.

Our analysis yields three intriguing results. First, from a modeling perspective, a VAR with stochastic volatility is shown to provide the best in-sample fit of the data. Next, positive energy price shocks have consistently generated economic stagflation over the past two decades. Interestingly, both the inflation and real GDP responses have consistently declined over the sample period. Next, the interest rate responses were found to be consistently positive over the sample period. Given the aforementioned stagflation result, this suggests that the PBOC is more focused on inflation stabilization as compared to facilitating output growth. All presented results were shown to be robust under both official national data and those developed by Chang et al. (2015), thus strengthening our conclusion that energy price shocks have significant time varying effects on China's macroeconomy.

The paper is organized as follow. In Section 2 we present the new energy price index and data sources. In Section 3 we outline the econometric methodology; including the various model specifications, in-sample model selection, identification and computation of the non-linear impulse response functions. Sections 4 and 5 contain discussions of the main results and various robustness checks. Finally, we conclude in Section 6.

Section snippets

Index creation

As highlighted in the introduction, a compelling index of quarterly energy prices must not only reflect fluctuations of international energy prices but also the dynamic structure of China's total energy expenditure shares. One natural way to incorporate these features into an energy price index is to utilize a simple dynamic weighted average of the world prices of coal, oil and natural gas in which the time varying weights are based on the annual share of total energy expenditure. To this end,

Empirical methodology

This section begins by describing the set of time varying VAR models used to distinguish between any time variation in the endogenous relationship between energy prices and the Chinese economy and any volatility clustering in the residuals.¹⁰ Once established, the first step in our empirical analysis is to conduct a formal Bayesian model comparison exercise to distinguish between relevant

Model comparison

This section presents an overview of the model comparison exercise used to select the best model for the given application. To this end, suppose that we wanted to compare the in-sample fit of an arbitrary model M_i, against a distinct model M_j. Each model M_k, k = i,j, is formally defined by a likelihood function, denoted by $p (y | θ_{k}, M_{k})$ , and a prior on the model-specific parameter vector θ_k, denoted by $p (θ_{k} | M_{k})$ . Given this information a formal method of model comparison is then based on the Bayes

Identification

It is well known that the identification of a structural VAR model is a subject of ongoing research.¹² Following Sims (1980), traditional identification of the SVAR model is completed by placing a recursive structure on the contemporaneous relationships between the variables in the system. This recursive identification strategy has been

GIRFs

To investigate the significance of time variation within the propagation mechanism of exogenous shocks we now conduct an intertemporal comparison of the resulting impulse response functions. A major difficulty in creating impulse response functions with time varying models is that they present non-linearities. More precisely, a direct consequence of allowing for time varying shocks is that a one standard deviation shock is not homogeneous amongst all time periods. This means that the scale of

Empirical results

We begin our analysis with a discussion of the formal Bayesian model selection exercise in which the VAR-SV model is found to be the best model. Having established this result, we then discuss the significance of allowing for time varying volatility. The section then concludes by utilizing the aforementioned identification procedure along with (generalized) impulse response functions to investigate the relationship between China's macroeconomy and energy price shocks.

Model comparison results

As discussed in Section 3.2, a formal way to choose between models in a Bayesian framework is to compare the respective Bayes Factor; a representation of the marginal likelihood. The model comparison results for the set of time-varying VARs is reported in Table 2. For interpretation purposes, the model with the largest log-ML is preferred. It is immediately obvious that the VAR-SV with one lag provides the best in-sample fit. In other words, the model comparison results suggest that stochastic

Estimated shock volatilities

Having discovered that the VAR-SV model provides the best in-sample fit for our application, we now examine the significance of allowing for time varying volatilities. The posterior means and 68% credible intervals are shown in Fig. 4.¹⁶

Three points are worth emphasizing. First, since none of the credible sets contain zero, the time varying standard deviations for all

Responses to an energy price shock

In this section we investigative the effect of energy price shocks on China's macroeconomy through the use of generalized impulse response functions. In line with the broader literature, the energy shocks are normalized to represent a 10% price increase.

Fig. 5 displays the intertemporal response of GDP growth. Despite only enforcing the sign restrictions on the contemporaneous responses, the shock consistently generates a reduction in the growth rate of real GDP over the sample period. This

Additional results and sensitivity analysis

While the median impulse response functions in the previous section provide an average depiction of the quantitative effects of energy price shocks in China's economy, they are silent on whether these effects are statistically significant. We therefore start the robustness section by examining the 68% credible interval of each GIRF at selected time periods. The results for each variable are provided in Fig. 11, Fig. 12, Fig. 13, Fig. 14. For interpretation purposes note that if the credible

Conclusion

In this paper, we examined whether energy price shocks have real effects on China's macroeconomy, over the past two decades. We began by highlighting the existence of two fundamental differences in the total energy expenditure shares on primary commodities: coal, crude oil and natural gas, within China and the US. First, total oil expenditure in the US constitutes around 70% of the primary commodity energy expenditure shares, with coal and natural gas contributing the remaining 30%. In

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Time varying macroeconomic effects of energy price shocks: A new measure for China☆

Highlights

Abstract

Introduction

Section snippets

Index creation

Empirical methodology

Model comparison

Identification

GIRFs

Empirical results

Model comparison results

Estimated shock volatilities

Responses to an energy price shock

Additional results and sensitivity analysis

Conclusion

Comput. Stat. Data Anal.

Econ. Lett.

Energy Econ.

Energy Policy

Energy Policy

J. Int. Money Financ.

J. Econ.

Energy Econ.

China Econ. Rev.

Energy Policy

J. Int. Money Financ.

China Econ. Rev.

Energy Econ.

J. Econ. Dyn. Control

J. Econ.

China Econ. Rev.

Energy Econ.

Energy Econ.

China Econ. Rev.

China Econ. Rev.

What drives oil prices? Emerging versus developed economies

J. Appl. Econ.

The role of time-varying price elasticities in accounting for volatility changes in the crude oil market

J. Appl. Econ.

Time-varying effects of oil supply shocks on the US economy

Am. Econ. J. Macroecon.

On Gibbs sampling for state space models

Biometrika

Bayesian model comparison for time-varying parameter vars with stochastic volatility

J. Appl. Econ.

Estimation of stochastic volatility models with heavy tails and serial dependence