Hierarchical shrinkage in time-varying parameter models

Belmonte, M. A. G., Koop, G. and Korobilis, D. (2014) Hierarchical shrinkage in time-varying parameter models. Journal of Forecasting, 33(1), pp. 80-94. (doi: 10.1002/for.2276)

80412.pdf - Accepted Version



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.

Item Type:Articles
Keywords:Forecasting, hierarchical prior, time-varying parameters, Bayesian Lasso
Glasgow Author(s) Enlighten ID:Korompilis Magkas, Professor Dimitris
Authors: Belmonte, M. A. G., Koop, G., and Korobilis, D.
Subjects:H Social Sciences > HA Statistics
H Social Sciences > HB Economic Theory
College/School:College of Social Sciences > Adam Smith Business School > Economics
Journal Name:Journal of Forecasting
ISSN (Online):1099-131X
Copyright Holders:Copyright © 2013 John Wiley and Sons, Ltd
First Published:First published in Journal of Forecasting 33(1):80-94
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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