Inducing Sparsity and Shrinkage in Time-Varying Parameter Models

35 Pages Posted: 6 Nov 2019

See all articles by Florian Huber

Florian Huber

University of Salzburg

Gary Koop

University of Strathclyde, Glasgow - Strathclyde Business School - Department of Economics

Luca Onorante

European Central Bank (ECB); European University Institute

Date Written: November, 2019

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.

Keywords: hierarchical priors, shrinkage, sparsity, time varying parameter regression

JEL Classification: C11, C30, E3, D31

Suggested Citation

Huber, Florian and Koop, Gary and Onorante, Luca, Inducing Sparsity and Shrinkage in Time-Varying Parameter Models (November, 2019). ECB Working Paper No. 2325. Available at SSRN: https://ssrn.com/abstract=3480397

Florian Huber (Contact Author)

University of Salzburg ( email )

Akademiestra├če 26
Salzburg, Salzburg 5020
Austria

Gary Koop

University of Strathclyde, Glasgow - Strathclyde Business School - Department of Economics ( email )

100 Cathedral Street
Glasgow G4 0LN
United Kingdom

Luca Onorante

European Central Bank (ECB) ( email )

Sonnemannstrasse 22
Frankfurt am Main, 60314
Germany

European University Institute

Villa Schifanoia
133 via Bocaccio
Firenze (Florence), Tuscany 50014
Italy

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