Hierarchical Shrinkage in Time-varying Parameter Models

Miguel Belmonte, Gary Koop, Dimitris Korobilis

Research output: Working paperDiscussion paper

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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.
Original languageEnglish
Place of PublicationGlasgow
PublisherUniversity of Strathclyde
Number of pages33
Publication statusPublished - 30 Jun 2011


  • forecasting
  • hierarchical prior
  • time-varying parameters
  • bayesian lasso

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