On the Predictability of China Macro Indicator with Carbon Emissions Trading

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

doi:10.3390/en14051271

Qian Chen, Shan Xie + Show 4 more

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https://doi.org/10.3390/en14051271

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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.

Highlights

We investigate whether our autoregressive distributed lag (ADL)-mixed data sampling (MIDAS) model with high-frequency carbon emissions trading data as inputs can outperform a benchmark auto-regressive (AR) model without such data
By setting the AR model as the first benchmark, we aim to evaluate whether our proposed ADL-MIDAS model with high-frequency carbon emission trading data perform better than models without such trading data in macro forecasts
The turned out an appropriate choice considering the significance of the coefficient and the multiple ADL-MIDAS model consistently yields smaller rRMSE than the single-predictor model fitting.model

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Predicting macro indicators has always been a hot area of research. This is because accurate and timely forecast serves as an important reference point for economic decisionmaking by both policymakers and investors. Giannone et al [1] find that the timeliness, as well as the quality of information, are helpful to increase prediction accuracy when generating nowcasts of quarterly GDP data using current-quarter data when they are immediately available. To deal with the fact that macroeconomic time series are often of different frequencies, researchers resort to the mixed data sampling (MIDAS) regressions

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