2022/01/18 by Joshua C. C. Chan, Chan, Joshua C. C. · 1 citation
Economics, Econometrics and Finance · Engineering · #Econometrics (econ.EM) #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Economics and business #Market Dynamics and Volatility #Methodology (stat.ME) #Monetary Policy and Economic Impact
paper · pdf · doi:10.48550/arxiv.2201.07303
openalex publication_date 2022/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Time-varying parameter VARs with stochastic volatility are routinely used for structural analysis and forecasting in settings involving a few endogenous variables. Applying these models to high-dimensional datasets has proved to be challenging due to intensive computations and over-parameterization concerns. We develop an efficient Bayesian sparsification method for a class of models we call hybrid TVP-VARs--VARs with time-varying parameters in some equations but constant coefficients in others. Specifically, for each equation, the new method automatically decides whether the VAR coefficients and contemporaneous relations among variables are constant or time-varying. Using US datasets of various dimensions, we find evidence that the parameters in some, but not all, equations are time varying. The large hybrid TVP-VAR also forecasts better than many standard benchmarks.