Hierarchical shrinkage in time-varying parameter models

Miguel A.G. Belmonte, Gary Koop and Dimitris Korobilis
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Miguel A.G. Belmonte: University of Srathclyde, U.K.

No 2011036, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

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 (search for similar items in EconPapers)
JEL-codes: C11 C52 E37 E47 (search for similar items in EconPapers)
Date: 2011-09-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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https://sites.uclouvain.be/core/publications/coredp/coredp2011.html (application/pdf)

Related works:
Journal Article: Hierarchical Shrinkage in Time‐Varying Parameter Models (2014)
Working Paper: Hierarchical Shrinkage in Time-Varying Parameter Models (2011)
Working Paper: Hierarchical shrinkage in time-varying parameter models (2011)
Working Paper: Hierarchical Shrinkage in Time-Varying Parameter Models (2011)
Working Paper: Hierarchical Shrinkage in Time-Varying Parameter Models (2011)
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