Before proceeding we address two natural concerns that may arise in regards to the creation of a new index. In the first instance, we discuss why previously established world energy price indexes, such as those published by the International Monetary Fund (IMF) and World Bank, are not appropriate when examining the effects of en- ergy price shocks on China’s macroeconomy. Next, we address the query of whether
84Time-varying Macroeconomic Effects of Energy Price Shocks: A New Measure for China
using international energy prices is an appropriate metric for our study.
To respond to the first of these concerns, first note that the IMF uses the volume of world export of crude oil, coal and natural gas product to compute weights for its energy price index which is updates about every five years. More precisely, the weights in this index are derived from the relative trade values compared to the total world trade as reported in the UN Comtrade database using a Laspeyres index approach in which crude oil, coal and natural gas are weighted 85 percent, 4 percent and 11 percent, respectively. Conversely, World Banks index is designed for low and middle income countries with fixed weighs of crude oil, coal and natural gas expenditure in which the weights are 4.7 percent and 10.8 percent. In light of these definitions, we note that while the primary energy commodities considered in our analysis are identical with those used in the IMF and World Bank, as discussed in the introduction, our objective is to design an index that accurately represents the evolving structure of the Chinese economy. To help illustrative this point, Figure 6.2 displays the comparison between the quarterly growth rate of the real Chinese energy price that we construct and the real global energy prices calculated based on data published by the IMF and World Bank. Two points are worth noting. First, the directions of the fluctuations in each of the indexes is quite similar. This comes about because the underlying price data in our index is identical to those used by the IMF and World Bank indexes. Next, the magnitude of these fluctuations is much smaller for the China specific energy index as compared to the IMF and World Bank counterparts. This result is reflective of the fact that each of the indexes use different weights in their calculations. This also because the weight of coal is higher for China, than most countries, while coal prices do not fluctuate much.4
To respond to the second query, we argue that the answer is yes; on the grounds that our sample commences in 1993. Prior to 1993, there was a little connection between the Chinese market and the broad global energy market. Since then, an increasing shortage in domestic energy supply has resulted in the gradual relaxation a centrally planned energy sector towards a market-oriented mechanism.5 This microeconomic reform started in the coal industry, where a system of dual track pricing was intro- duced as an incentive to increase coal production. One year later, the oil market also received a major reform in which state-regulated prices were completely replaced by an international price mechanism. A similar reform for the natural gas industry came in 1997. Empirical support for the integration of China’s energy industry with the
4Before 2014, coal prices were determined 2 year contrasts, thus, the prices were not spot prices.
5For an overview of China’s energy product pricing mechanism see, for example, He et al. [2013b];
§5.2 Data and index creation 85 1995 2000 2005 2010 2015 -20 -15 -10 -5 0 5 10 Percent
Chinese Energy Price IMF Energy Price WB Energy Price
Figure 5.3: The growth rate of Chinese energy price (by authors) and global energy price (by the IMF and World Bank)
international energy markets has been provided by He et al. [2013b], who find a high degree of correlation between Chinese and international oil prices. For instance, do- mestic oil prices closely follow the movement of global oil prices with the lag of one or two months. Their study also finds high degrees of co-movement between Chinese coal prices are the price of coal in Australia. More recently, Zhang and Xie [2016] fur- ther investigate the energy pricing mechanism in China, concluding that domestic prices tend to follow international prices. They suggest that economic agents have learned that the domestic prices closely follow their international counterparts and thus form expectations based on fluctuations in global energy markets.
5.2.3 Data
To analyze the relationship between energy price shocks and China’s macroeconomy, we use the following set of macroeconomic variables: real GDP, GDP deflator infla- tion and a short-term interest rate. All data in the main study is sourced from the CEIC Asia Database in which the original source is the National Bureau of Statistics of China and The People’s Bank of China (PBOC).6 To capture the cyclical compo- nent of the data, both Real GDP and inflation are are linearly de-trended prior to estimation.
To address concerns relating to the quality of official Chinese macroeconomic data
6Since The National Bureau of Statistics of China only publishes quarterly real GDP or GDP deflator
from 2001. For this reason, real GDP is obtained by adjusting nominal GDP by the GDP deflator, where the latter variable is sourced from the Federal Research Bank of Atlanta website.
86Time-varying Macroeconomic Effects of Energy Price Shocks: A New Measure for China
(see, e.g., Fernald et al. [2013]; Nakamura et al. [2014]; Holz [2014] or Cross and Nguyen [2017a]), all main results are subjected to a sensitivity analysis in which we re-estimate the models with real GDP and inflation data as developed by Chang et al. [2015].7 Next, recent literature has highlighted the fact that The PBOC has used a range of different monetary instruments to conduct its policy.8 For instance, Fernald
et al. [2014] use a broad set of Chinese economic indicators from 2000M1 to 2013M9 and provide evidence that Chinese monetary policy in China has moved closer to those of Western market economies through the utilization of a single bank rate. Following this result, the short-term, interest rate in our main analysis is taken to be the PBOC bank rate (less than 20 days). Sensitivity analysis of these results by using the required reserve ratio as alternative measures of monetary policy is presented in Section 5.5.
5.3
Empirical methodology
This section begins by describing the set of Bayesian time varying VAR models used to distinguish between time variation in the endogenous relationship between energy prices and the Chinese economy and any volatility in the innovations. Once estab- lished, the first step in our empirical analysis is to conduct a formal Bayesian model comparison exercise to distinguish between relevant features of the data. Having established the best model, we then propose a new set of agnostic sign restrictions to identify a set of structural shocks. These structural shocks are then used within gen- eralized impulse response functions to investigate the effects of energy price shocks on China’s macroeconomy.