混频数据信息下的时变货币政策传导行为研究——基于混频 TVP-FAVAR模型  

The Time-varying Transmission Mechanism of Monetary Policy with Mixed Frequency Data:Evidence from MF-TVP-FAVAR Model

作　　者：尚玉皇[1] 赵芮 董青马[1] SHANG Yuhuang;ZHAO Rui;DONG Qingma(Institute of Chinese Financial Studies,Southwestern University of Finance and Economics)

机构地区：[1]西南财经大学中国金融研究中心,四川成都611130

出　　处：《金融研究》2021年第1期13-30,共18页Journal of Financial Research

基　　金：国家自科基金青年项目(71701165);国家社科基金一般项目(20BJY255);国家社科基金重大项目(20&ZD081);国家自科基金面上项目(71973110);国家自科基金专项项目(71950010)资助。

摘　　要：现实经济环境中,货币政策操作受到金融市场及宏观经济信息的共同影响。如何基于混频大数据信息分析货币政策行为机制是需解决的现实问题。为此,本文提出一种混频时变参数因子增广向量自回归(MF-TVP-FAVAR)模型。基于宏观经济及金融市场等多维度混频数据信息的实证结果表明:首先,MF-TVP-FAVAR模型在宏观金融混频数据中提取的金融形势指数(FCI)能较好地表征宏观经济先行趋势,为货币政策的制定提供前瞻性信息。其次,混频TVP-FAVAR模型可以捕捉价格型和数量型货币政策传导的高频时变特征。与货币供应量相比,利率传导对产出的影响具有滞后性。利率传导随着利率市场化改革愈发畅通,而信贷传导机制因财政政策搭配等问题日渐受阻。再次,货币政策传导效果受到经济周期影响,无论产出效应还是价格效应,经济上行时期,货币政策传导机制都比经济衰退时期更加通畅。价格型和数量型传导机制在经济下行时的作用效果均会减弱,但数量型货币政策更易受到经济周期的影响。最后,货币政策对FCI的冲击响应具有时变性,说明金融市场信息冲击对我国货币政策调控具有结构性的动态影响。货币当局制定尤其是微调货币政策时应及时评估金融市场信息冲击的影响。The global economy is facing increasing uncertainty,and the financial market is becoming more fragile.China's macro-economy is also facing problems such as economic structural adjustment and financial risk agglomeration,which make the relationship between monetary policy and macro-economy more challenging.The gradual reform of Chinese interest rate marketization and the rapid development of Fintech are also leading to a complex financial big data environment in terms of monetary policy.To understand the dynamic behavior of the monetary policy transmission mechanism,the available macroeconomic and financial market big data information must be utilized.Effectively analyzing the monetary policy mechanism through big data is thus a critical problem.The transmission mechanism for monetary policies attracts extensive research attention.Some believe that the credit(quantitative)transmission mechanism is the main factor,while others suggest that the interest rate mechanism is more effective.Monetary policies typically exhibit time-varying features due to the business cycle,and thus a time-varying parameter vector autoregression(TVP-VAR)model is proposed to capture the behavior of monetary policies.The factor-augmented VAR(FAVAR)model is also used to analyze monetary policy,as it effectively utilizes real economic data.The traditional TVP-FAVAR model uses only the same frequency data.However,the frequency of macroeconomic data is completely different from that of financial market data.Mixed frequency data are therefore widespread in actual economic activities.Effectively using such data to construct a TVP-FAVAR model and then analyze the monetary policy behavioral mechanism is therefore the challenge we face,and the aim of this study.We propose a mixed frequency TVP-FAVAR(MF-TVP-FAVAR)model.We collect Chinese mixed frequency big data for our empirical study.The main advantage of the MF-TVP-FAVAR model is that it maximizes the integration of high-frequency financial market information and low-frequency macroeconomic inform

关 键 词：TVP-FAVAR模型 混频数据 货币政策 传导机制 

分 类 号：F822.0[经济管理—财政学] F224