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Hierarchical shrinkage in time-varying parameter models

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  • Miguel, Belmonte
  • Gary, Koop
  • Dimitris, Korobilis

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.

Suggested Citation

  • Miguel, Belmonte & Gary, Koop & Dimitris, Korobilis, 2011. "Hierarchical shrinkage in time-varying parameter models," MPRA Paper 31827, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:31827
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    References listed on IDEAS

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    More about this item

    Keywords

    Forecasting; hierarchical prior; time-varying parameters; Bayesian Lasso;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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