Hierarchical Shrinkage in Time-Varying Parameter Models

28 Pages Posted: 29 Jun 2011

See all articles by Miguel Belmonte

Miguel Belmonte

affiliation not provided to SSRN

Gary Koop

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

Dimitris Korobilis

University of Glasgow - Adam Smith Business School

Date Written: June 23, 2011

Abstract

In this paper, we forecast EU-area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter-rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: i) time varying, ii) constant over time or iii) shrunk to zero. The econometric methodology decides automatically which category each coefficient belongs in. Our empirical results indicate the benefits of such an approach.

Keywords: Forecasting, hierarchical prior, time-varying parameters, Bayesian Lasso

JEL Classification: C11, C52, E37, E47

Suggested Citation

Belmonte, Miguel and Koop, Gary and Korobilis, Dimitris, Hierarchical Shrinkage in Time-Varying Parameter Models (June 23, 2011). Available at SSRN: https://ssrn.com/abstract=1874159 or http://dx.doi.org/10.2139/ssrn.1874159

Miguel Belmonte

affiliation not provided to SSRN ( email )

Gary Koop

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

100 Cathedral Street
Glasgow G4 0LN
United Kingdom

Dimitris Korobilis (Contact Author)

University of Glasgow - Adam Smith Business School ( email )

40 University Avenue
Gilbert Scott Building
Glasgow, Scotland G12 8QQ
United Kingdom

HOME PAGE: http://https://sites.google.com/site/dimitriskorobilis/

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