Inducing Sparsity and Shrinkage in Time-Varying Parameter Models
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.
Co-authored with Florian Huber and Luca Onorante
About Gary Koop
Gary Koop is a professor in the Department of Economics at the University of Strathclyde. He received his PhD at the University of Toronto in 1989. His research work in Bayesian econometrics has resulted in numerous publications in top econometrics journals such as the Journal of Econometrics. He has also published several textbooks including Bayesian Econometrics, Bayesian Econometric Methods and is co-editor of the Oxford Handbook of Bayesian Econometrics. He is on the editorial board of several journals including the Journal of Business and Economic Statistics and the Journal of Applied Econometrics.
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