Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/208359
Authors: 
Huber, Florian
Koop, Gary
Onorante, Luca
Year of Publication: 
2019
Series/Report no.: 
ECB Working Paper 2325
Abstract: 
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.
Subjects: 
Sparsity
shrinkage
hierarchical priors
time varying parameter regression
JEL: 
C11
C30
E3
D31
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-3894-5
Document Type: 
Working Paper

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