Journal Article

On the Predictability of China Macro Indicator with Carbon Emissions Trading

Xiang Gao, Shan Xie, Shuairu Tian, Qian Chen, Li Sun, Shigeyuki Hamori

2021

Overview

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.

• Publisher: MDPI AG
• Subject: GDP growth / high-frequency carbon emissions trading / macroeconomic forecast / mixed data sampling regression / out-of-sample prediction / purchasing managers’ index