Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/208359 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
ECB Working Paper No. 2325
Verlag: 
European Central Bank (ECB), Frankfurt a. M.
Zusammenfassung: 
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.
Schlagwörter: 
Sparsity
shrinkage
hierarchical priors
time varying parameter regression
JEL: 
C11
C30
E3
D31
Persistent Identifier der Erstveröffentlichung: 
ISBN: 
978-92-899-3894-5
Dokumentart: 
Working Paper

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