货币政策的不确定性、宏观经济波动与银行系统金融风险——基于政策不确定性指标不同测度和Proxy-SVAR实证方法的研究  

Monetary Policy Uncertainty,Macroeconomic Fluctuations,and Systemic Bank Risk——Based on Different Measures of Policy Uncertainty Indicators and the Empirical Analysis of Proxy-SVAR

作　　者：陆前进[1] 李欣 李晓康 LU Qian-jin;LI Xin;LI Xiao-kang(School of Economics,Fudan University 200433;Postdoctoral Research Workstation of Bank of Communications,200120)

机构地区：[1]复旦大学经济学院,200433 [2]交通银行博士后科研工作站,200120

出　　处：《上海经济研究》2024年第12期101-116,共16页Shanghai Journal of Economics

基　　金：国家社会科学基金项目“人民币国际化与货币政策的跨国溢出效应研究”(批准号:23BJY122)阶段性成果之一。

摘　　要：建立了一个中等规模的结构向量自回归模型,系统探讨了货币政策不确定性对宏观经济波动与银行系统金融风险的影响。首先,比较现阶段我国主流货币政策不确定性及经济政策不确定性代理指标,发现Huang&Luk (2020)所测度MPU与EPU并不能对我国宏观经济波动做出较好解释。比较而言,Baker et al.(2016)与Davis et al.(2019)的EPU对我国宏观经济波动影响更符合预期,EPU的影响类似于紧缩性供给冲击,导致产出、固定资产投资以及社会零售消费降低,通货膨胀上升。特别是在EPU_Davis指标存在意外正向冲击时,可能导致银行系统金融风险上升。其次,利用条件波动率思想,重新估计货币政策不确定性(MPU_Volatility),发现无论是简约形式或结构形式估计,所获得MPU_Volatility在波动趋势上大体一致,且MPU_Volatility与EPU_Baker及EPU_Davis对宏观经济作用效果存在较大一致,其中MPU_Volatility与EPU_Davis对银行系统金融风险影响一致。最后,进一步将叙事性EPU_Davis指标与MPU_Volatility结合使用,选用Proxy SVAR对货币政策不确定性结构冲击进行识别,这保证了即使存在测量误差情况下,估计偏误也会大幅降低。实证发现MPU对宏观经济变量造成不利影响,导致银行系统金融风险上升并持续较长时间。脉冲响应结果与EPU_Davis及MPU_Volatility结果相似,这一定程度上支持之前结论的稳健。A medium-scale structural vector autoregressive(SVAR) model is established to investigate the impact of monetary policy uncertainty on macroeconomic fluctuations and systemic bank risk. Firstly, we compare the current proxies for monetary policy uncertainty(MPU) and economic policy uncertainty(EPU) in China, finding that the measures by Huang & Luk(2020) for MPU and EPU do not adequately explain macroeconomic fluctuations. In contrast, proxies like EPU by Baker et al.(2016) and Davis et al.(2019) exhibit more expected impacts on macroeconomic fluctuations in China, akin to contractionary supply shocks leading to declines in output, fixed asset investment, and social retail consumption with an increase in inflation. In particular, unexpected positive shocks in EPU_Davis may lead to heightened systemic bank risk. Secondly, adopting the concept of conditional volatility, we reassess MPU, finding consistent trends in MPU_Volatility whether estimated in reduced or structural forms, with significant alignment in its effects on macroeconomic variables with EPU_Baker and EPU_Davis. Notably, MPU_Volatility shows consistent effects with EPU_Davis on systemic bank risk. Finally, by integrating MPU_Volatility with narrative EPU_Davis indicators, we employ a Proxy SVAR approach, mitigating estimation biases even under measurement errors, to identify structural shocks in monetary policy uncertainty. Empirical findings suggest that MPU adversely affects macroeconomic variables, leading to prolonged increases in systemic bank risk. Impulse response results align closely with those of EPU_Davis and MPU_Volatility, reinforcing the robustness of earlier conclusions.

关 键 词：货币政策不确定性 银行系统金融风险 虚拟变量结构向量自回归 结构冲击识别 测量误差 

分 类 号：F832[经济管理—金融学]