Time-Varying Vector Autoregressive Models with Structural Dynamic Factors
We suggest a simple methodology to estimate time-varying parameter vector autoregressive (VAR) models. In contrast to the widely used Bayesian approach, our approach is based on combining a dynamic factor model for the VAR coefficient matrices and a score-driven model for the time-varying variances. Our algorithm is robust and fast, while being easy to implement. In a small simulation study, we demonstrate the good performance of the method. Furthermore, using the empirical data set on U.S. macroeconomic and financial variables that is also used in Prieto et al. (2016), we show that our approach is promising in modeling time-varying macro-financial linkages.
(joint work with Paolo Gorgi and Siem Jan Koopman)