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Inducing Sparsity and Shrinkage in Time-varying Parameter Models

Florian HuberGary KoopLuca Onorante

出版	European Central Bank, 2019
ISBN	92899389439789289938945
URL	http://books.google.com.hk/books?id=QnOlzQEACAAJ&hl=&source=gbs_api

註釋

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.