Inducing Sparsity and Shrinkage in Time-Varying Parameter Models
35 Pages Posted: 6 Nov 2019
Date Written: November, 2019
Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when the number of variables in the model is large. Global-local priors are increasingly used to induce shrinkage in such models. But the estimates produced by these priors can still have appreciable uncertainty. Sparsification has the potential to remove this uncertainty and improve forecasts. In this paper, we develop computationally simple methods which both shrink and sparsify TVP models. In a simulated data exercise we show the benefits of our shrink-then-sparsify approach in a variety of sparse and dense TVP regressions. In a macroeconomic forecast exercise, we find our approach to substantially improve forecast performance relative to shrinkage alone.
Keywords: hierarchical priors, shrinkage, sparsity, time varying parameter regression
JEL Classification: C11, C30, E3, D31
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