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Open AccessArticle

On the Predictability of China Macro Indicator with Carbon Emissions Trading

1
Business School, Shenzhen Technology University, 3002 Lantian Road, Pingshan District, Shenzhen 518118, China
2
Research Center of Finance, Shanghai Business School, 2271 West Zhongshan Road, Shanghai 200235, China
3
Yangtze Ecology and Environment Co., Ltd., Liuhe Road, Jiangan District, Wuhan 430014, China
4
Graduate School of Economics, Kobe University, 2-1, Rokkodai, Nada-Ku, Kobe 657-8501, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Victor Moutinho
Energies 2021, 14(5), 1271; https://doi.org/10.3390/en14051271
Received: 5 January 2021 / Revised: 18 February 2021 / Accepted: 19 February 2021 / Published: 25 February 2021
(This article belongs to the Special Issue Time Series Analysis of Energy Economics)
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. View Full-Text
Keywords: high-frequency carbon emissions trading; macroeconomic forecast; mixed data sampling regression; GDP growth; purchasing managers’ index; out-of-sample prediction high-frequency carbon emissions trading; macroeconomic forecast; mixed data sampling regression; GDP growth; purchasing managers’ index; out-of-sample prediction
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MDPI and ACS Style

Chen, Q.; Gao, X.; Xie, S.; Sun, L.; Tian, S.; Hamori, S. On the Predictability of China Macro Indicator with Carbon Emissions Trading. Energies 2021, 14, 1271. https://doi.org/10.3390/en14051271

AMA Style

Chen Q, Gao X, Xie S, Sun L, Tian S, Hamori S. On the Predictability of China Macro Indicator with Carbon Emissions Trading. Energies. 2021; 14(5):1271. https://doi.org/10.3390/en14051271

Chicago/Turabian Style

Chen, Qian; Gao, Xiang; Xie, Shan; Sun, Li; Tian, Shuairu; Hamori, Shigeyuki. 2021. "On the Predictability of China Macro Indicator with Carbon Emissions Trading" Energies 14, no. 5: 1271. https://doi.org/10.3390/en14051271

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