Template-Type: ReDIF-Paper 1.0
Author-Name: Miguel Belmonte
Author-Name-First: Miguel
Author-Name-Last: Belmonte
Author-Email: miguel.belmonte@strath.ac.uk
Author-Workplace-Name: Department of Economics, University of Strathclyde
Author-Name: Gary Koop
Author-Name-First: Gary
Author-Name-Last: Koop
Author-Email: gary.koop@strath.ac.uk
Author-Workplace-Name: Department of Economics
Author-Name: Dimitris Korobilis
Author-Name-First: Dimitris
Author-Name-Last: Korobilis
Author-Email: Dimitris.Korobilis@glasgow.ac.uk
Author-Workplace-Name: Department of Economics, University of Glasgow
Title: Hierarchical Shrinkage in Time-Varying Parameter Models
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.
Length: 33 pages
Creation-Date: 2011-06
Revision-Date: 
Publication-Status: Published
File-URL: http://www.strath.ac.uk/media/1newwebsite/departmentsubject/economics/research/researchdiscussionpapers/2011/11-37-Final.pdf
File-Format: Application/pdf
Number: 1137
Classification-JEL: C11, C52, E37, E47
Keywords: Forecasting; hierarchical prior; time-varying parameters; Bayesian Lasso
Handle: RePEc:str:wpaper:1137