The impact of high-frequency economic policy uncertainty on China’s macroeconomy: evidence from mixed-frequency VAR

Abstract

We investigate the impact of high-frequency economic policy uncertainty on investments of state-owned and private-owned enterprises (SOEs and POEs), as well as short-, medium- and long-term bank loans in China by employing the mixed-frequency vector autoregression model. Impulse response analysis suggests that monthly economic policy uncertainty is allowed to have heterogeneous effects on investments and bank loans in China. Variance decomposition analysis shows that aggregating monthly economic policy uncertainty into the quarterly level underestimates the influence of economic policy uncertainty in shaping China’s macroeconomy at business cycle frequencies. By further decomposing the SOEs’ investment, we reveal that the effects of economic policy uncertainty on SOEs’ investment are strengthened due to the existence of the injection of the government investment into SOEs. Trade policy uncertainty has a similar impact on China’s investments and bank loans as economic policy uncertainty. The counterfactual analysis shows that the impact of economic policy uncertainty on China’s investments and bank loans is alleviated when the interest rate channel exists. Our major conclusions are insensitive to a series of robustness checks.

Keywords:

• Economic policy uncertainty
• state-owned enterprises
• bank loans
• trade policy uncertainty
• counterfactual analysis
• mixed-frequency vector autoregression

JEL Classification::

• C32
• E22
• E44

Acknowledgments

I would like to thank all anonymous referees for their valuable suggestions.

Disclosure statement

No potential conflict of interest was declared by the authors.

Notes

1 State-owned enterprises (SOEs) in China refer to firms owned by all citizens of China and controlled by central and local governments. Usually, the objectives of SOEs go beyond profits, including resources, employment, and foreign policy. In China, some SOEs controlled by the central government (e.g., State Grid Corporation of China) and most SOEs controlled by the local governments are nonlisted firms. Private-owned enterprises (POEs) refer to firms excluding state-owned enterprises.

2 Baker et al. (2019) point out that the recent rise in trade policy uncertainty threatens to become the new normal and trade policy uncertainty further generates negative effects on firm-level and macroeconomic performance.

3 Although Liu and Zhang (2015) show that EPU leads to significant increases in China’s stock market volatility, we should be cautious about the results of Liu and Zhang (2015). Liu and Zhang (2015) only cover the period from January 2, 1996 to June 24, 2013, which belongs to the periods that there is a close comovement between EPU and stock market volatility. When we turn to focus on recent periods, such as the US-China trade conflict, it is straightforward to find that the correlation between EPU and stock market volatility decreases substantially.

4 Similarly, based on the evidence of the US, Caggiano et al. (2014) estimate a smoothed transition VAR and find that the effects of uncertainty shocks are asymmetric over the business cycle in that unemployment and inflation react more to uncertainty shocks during recessions than they do during expansions.

5 By performing the stationary test, including the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root test, we take logarithm for all variables except the interest rate. We use the level of interbank offered rate as the interest rate. Also, we keep the log-level of EPU but use the log-difference of SOEs’ investment, POEs’ investment, short-term bank loans, as well as, medium- and long-bank loans.

6 As Motegi and Sadahiro (2018) point out, MF-VAR is primarily used for a small ratio of sampling frequencies, e.g., monthly/quarterly, rather than large ratio, e.g., daily/quarterly. Consider the EPU index is measured at a monthly frequency, therefore, the MF-VAR model satisfies our need for the subsequent analysis.

7 Davis et al. (2019) also construct the monthly trade policy uncertainty (TPU) index for China runs from January 2000 to the present. We rely on this index to analyze the impact of trade policy uncertainty. We should notice that Huang and Luk (2020) also construct a broad economic policy uncertainty in China on the basis of 114 newspapers published in mainland China. However, the length of EPU constructed by Huang and Luk (2020) starts from January 2000, therefore, this index is too short for our benchmark analysis.

8 Including Figures (1,2), (1,3), (1,4), (2,1), (2,2), and (2,4).

Additional information

Funding

This study was funded by National Social Sciences Funds (20BTJ015).