On the Predictability of China Macro Indicator with Carbon Emissions Trading

Qian Chen, Xiang Gao, Shan Xie, Li Sun, Shuairu Tian and Shigeyuki Hamori
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Qian Chen: Business School, Shenzhen Technology University, 3002 Lantian Road, Pingshan District, Shenzhen 518118, China
Shan Xie: Yangtze Ecology and Environment Co., Ltd., Liuhe Road, Jiangan District, Wuhan 430014, China
Li Sun: Research Center of Finance, Shanghai Business School, 2271 West Zhongshan Road, Shanghai 200235, China
Shuairu Tian: Research Center of Finance, Shanghai Business School, 2271 West Zhongshan Road, Shanghai 200235, China

Energies, 2021, vol. 14, issue 5, 1-24

Abstract: Accurate and timely macro forecasting requires new and powerful predictors. Carbon emissions data with high trading frequency and short releasing lag could play such a role under the framework of mixed data sampling regression techniques. This paper explores the China case in this regard. We find that our multiple autoregressive distributed lag model with mixed data sampling method setup outperforms either the auto-regressive or autoregressive distributed lag benchmark in both in-sample and out-of-sample nowcasting for not only the monthly changes of the purchasing managers’ index in China but also the Chinese quarterly GDP growth. Moreover, it is demonstrated that such capability operates better in nowcasting than h-step ahead forecasting, and remains prominent even after we account for commonly-used macroeconomic predictive factors. The underlying mechanism lies in the critical connection between the demand for carbon emission in excess of the expected quota and the production expansion decision of manufacturers.

Keywords: high-frequency carbon emissions trading; macroeconomic forecast; mixed data sampling regression; GDP growth; purchasing managers’ index; out-of-sample prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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