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| The Time-varying Transmission Mechanism of Monetary Policy with Mixed Frequency Data: Evidence from MF-TVP-FAVAR Model |
| SHANG Yuhuang, ZHAO Rui, DONG Qingma
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| Institute of Chinese Financial Studies, Southwestern University of Finance and Economics |
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Abstract 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 information, and effectively extracts unobservable potential factors from a large amount of information. These advantages help us to more accurately analyze the time-varying relationships of monetary policy, macro indicators, and financial market indicators.
The mixed frequency data are mainly derived from China's quarterly and monthly macro data, and monthly financial data are also included. The sample period is from January 1997 to December 2017. The data sources are the National Bureau of Statistics and the WIND database.
The main conclusions of this paper are as follows. First, based on the MF-TVP-FAVAR model, the Financial Condition Index (FCI) extracted from financial market big data can better establish the dynamics of China's financial situation. This index is a leading indicator that can be used to measure economic performance, and an auxiliary indicator of the intermediary target of monetary policy. The FCI has a significant positive impact on interest rates and money supply, and this impact shows time-varying features.
Second, based on the time-varying response function of monetary policy shocks, the MF-TVP-FAVAR model captures the time-varying features of the impact of monetary policy at a macroeconomic level. This impact can be identified through the monthly observation frequency, which significantly improves the timeliness of the monetary policy transmission mechanism. Unlike the money supply, the impact of interest rate transmission on output shows a lag effect. Interest rate transmission has become smoother with interest rate marketization, while the credit transmission mechanism is increasingly blocked by fiscal policy coordination.
Finally, the business cycle has a significant impact on the transmission mechanism of monetary policy. We find that both the output effect and the price effect of this mechanism are more fluent during an economic boom than in a recession. Thus, monetary policy transmission is obviously cyclical. However, compared with price-based monetary policies, quantitative monetary policies are more susceptible to the impact of the business cycle.
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Received: 25 March 2019
Published: 02 February 2021
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