Title: A latent threshold approach to large-scale mixture innovation models
Authors: Florian Huber - WU Vienna University of Economics and Business (Austria)
Gregor Kastner - WU Vienna University of Economics and Business (Austria) [presenting]
Martin Feldkircher - Oesterreichische Nationalbank (Austria)
Abstract: A straightforward algorithm is proposed to carry out inference in large time-varying parameter vector autoregressions (TVP-VARs) with mixture innovation components for each coefficient in the system. We significantly decrease the computational burden by approximating the latent indicators that drive the time-variation in the coefficients with a latent threshold process that depends on the absolute size of the shocks. The merits of our approach are illustrated with two applications. First, we forecast the US term structure of interest rates and demonstrate forecast gains of the proposed mixture innovation model relative to other benchmark models. Second, we apply our approach to US macroeconomic data and find significant evidence for time-varying effects of a monetary policy tightening.