2019/05/26 by Florian Huber, Gary Koop, Huber, Florian +3 · 2 citations
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Econometrics (econ.EM) #FOS: Economics and business #Forecasting Techniques and Applications #Monetary Policy and Economic Impact #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1905.10787
openalex publication_date 2019/05/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 reduce 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 forecasting exercise, we find our approach to substantially improve forecast performance relative to shrinkage alone.