Dynamic measurement of news-driven information friction in China's carbon market: Theory and evidence

Heng-Guo Zhang, Tingting Cao, Houxuan Li and Tiantian Xu

Energy Economics, 2021, vol. 95, issue C

Abstract: This paper dynamically measures news-driven information friction in China's carbon market theoretically and empirically. The biggest difference between this paper and the existing literature is that this paper does not assume that the information shock is a white noise process but that the information shock comes from the news. This study does not use fiscal expenditure budget data and fiscal expenditure final data to estimate information friction but rather extends the standard Fève and Pietrunti (2016) theoretical framework to identify effective information and noise signals from market policy news by machine learning. A latent Dirichlet allocation model is employed to estimate the variance of effective information. China's carbon policy uncertainty as indicated by Chinese-language newspapers is used to estimate the variance of the noise signal. Therefore, we can calculate time-varying high-frequency information friction. The results of this study show that enterprises with different attributes in the same industry face different information friction. In China, state-owned industrial enterprises have the lowest levels of information friction. Joint-stock industrial enterprises have the highest levels of information friction. The higher the degree of policy information friction, the weaker the impact of effective information shocks and the greater the reduction in policy effects. These results have policy implications – the government should pay attention to enterprise attributes to reduce policy uncertainty and the degree of policy information friction, thereby enhancing the effectiveness of policy regulation.

Keywords: DSGE model; Information frictions; Machine learning; News (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:95:y:2021:i:c:s0140988320303340

DOI: 10.1016/j.eneco.2020.104994

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