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Hierarchical shrinkage in time-varying parameter models

Author

Listed:
  • BELMONTE, Miguel A.G.

    (University of Srathclyde, U.K.)

  • KOOP, Gary

    (University of Strathclyde and the Rimini Center for Economic Analysis, U.K.)

  • KOROBILIS, Dimitris

    () (Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium)

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

  • BELMONTE, Miguel A.G. & KOOP, Gary & KOROBILIS, Dimitris, 2011. "Hierarchical shrinkage in time-varying parameter models," CORE Discussion Papers 2011036, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Handle: RePEc:cor:louvco:2011036
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    File URL: http://uclouvain.be/cps/ucl/doc/core/documents/coredp2011_36web.pdf
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    References listed on IDEAS

    as
    1. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    2. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
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    More about this item

    Keywords

    forecasting; hierarchical prior; time-varying parameters; Bayesian Lasso;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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