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Inducing Sparsity and Shrinkage in Time-Varying Parameter Models

Author

Listed:
  • Huber, Florian

    () (University of Salzburg)

  • Koop, Gary

    () (University of Strathclyde)

  • Onorante, Luca

    () (European Central Bank)

Abstract

Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when the number of variables in the model is large. Global-local priors are increasingly used to induce shrink- age in such models. But the estimates produced by these priors can still have appreciable uncertainty. Sparsification has the potential to remove this uncertainty and improve forecasts. In this paper, we develop computationally simple methods which both shrink and sparsify TVP models. In a simulated data exercise we show the benefits of our shrink-then-sparsify approach in a variety of sparse and dense TVP regressions. In a macroeconomic forecast exercise, we find our approach to substantially improve forecast performance relative to shrinkage alone.

Suggested Citation

  • Huber, Florian & Koop, Gary & Onorante, Luca, 2019. "Inducing Sparsity and Shrinkage in Time-Varying Parameter Models," Working Papers in Economics 2019-2, University of Salzburg.
  • Handle: RePEc:ris:sbgwpe:2019_002
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    Keywords

    Sparsity; shrinkage; hierarchical priors; time varying parameter regression;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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