Speaker: Tingguo Zheng(Professor, The Wang Yanan Institute of Studies in Economics,Xiamen University)
Description：This paper proposes a fast approach for estimating a large time-varying parameter structural vector autoregressive (TVP-SVAR) model. 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 forgetting factors, the filtering estimation of time-varying parameters is equivalent to an equation-by-equation estimator, which can significantly reduce the dimension of state space and thus is a very fast estimation. Moreover, an extremely fast smoothing estimation can be derived straightforwardly, overcoming the inverse of the supra-high dimensional state equation covariance matrix. We provide dynamic model averaging (selection) and maximum likelihood estimates for the needs of forecasting and inference, respectively. Our simulation study shows that the proposed method is more accurate than the existing popular methods and illustrates the tremendous computational gain from the equation-by-equation estimator. Finally, we conduct an empirical study on the dynamic connectedness of global stock markets, demonstrating our method's advantages in real-time and ex-post analysis.
Time：December08, 2022（Thursday）,14:00-16:00
Venue: Tencent meeting Room ID：736268185
