General Bayesian time-varying parameter VARs for predicting government bond yields
Manfred M. Fischer,
Niko Hauzenberger,
Florian Huber
et al.
Abstract:Time-varying parameter (TVP) regressions commonly assume that timevariation in the coefficients is determined by a simple stochastic process such as a random walk. While such models are capable of capturing a wide range of dynamic patterns, the true nature of time variation might stem from other sources, or arise from different laws of motion. In this paper, we propose a flexible TVP VAR that assumes the TVPs to depend on a panel of partially latent covariates. The latent part of these covariates differ in the… Show more
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