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郑挺国: Fast Estimation of a Large TVP-VAR Model with Score-Driven Volatilities

Release time: 2021-10-26      clicks:

郑挺国: Fast Estimation of a Large TVP-VAR Model with Score-Driven Volatilities

TimeFriday, July 23, 930-11:30 a.m. Beijing Time

Location1111, building 1, School of Management

SpeakerTingguo Zheng, Xiamen University

Abstract

This paper introduces a large time-varying parameter structural vector autoregressive (TVP-SVAR) model and then proposes a fast approach to estimate it. Based on the score-driven modeling framework, we firstly assume that the time-varying variances of structural errors in each equation of the TVP-SVAR are score-driven, and then propose the filtering and smoothing procedures for estimating time-varying parameters and time-varying volatilities. We show that under the Minnesota prior and forgetting factors, the filtering estimation of time-varying parameters is equivalent to an equation-by-equation estimator, which can greatly reduce the dimension of state space and thus is a very fast estimation. Moreover, we find that under forgetting factors, the smoothing estimation is also straightforward and extremely fast, which overcomes the inverse of supra-high dimensional state equation covariance matrix. Our simulation study shows that the proposed method in filtering the data is more accuracy than the existing popular method and illustrates the computational gain from the equation-by-equation estimator. Finally, we carry out an empirical study on dynamic connectedness of global stock markets, which demonstrates the advantages of our method in real-time and ex-post analysis.


Introduction

Professor Zheng Tingguo works at Xiamen University School of Economics and Wang Yanan Institute of Economics. He is a doctoral supervisor and a distinguished professor of Xiamen University. He has been successively selected as New Century Excellent Talents of the Ministry of Education, Yangtze River Scholars and Young Scholars of the Ministry of Education, and Young Top Talents of the National Ten Thousand Talents Program. Mainly engaged in research in the fields of macroeconomics and policy analysis, macroeconomics, financial econometrics, and time series analysis. In "Economic Research", "World Economy", "Economics Quarterly", "Financial Research", "Management World", Journal of Econometrics, Journal of Business & Economic Statistics, Journal of Multivariate Analysis, China Economic Review, etc. Nearly 70 papers have been published in the journal. Presided over 3 projects of the National Natural Science Foundation of China, and won the National Outstanding Doctoral Dissertation Nomination Award and many other provincial and ministerial awards. At present, he is committed to special research on macroeconomic and financial market monitoring, macroeconomic and financial big data analysis, and actively uses econometric methods to monitor and forecast the real economy in real time.