Inducing sparsity and shrinkage in time-varying parameter models

Florian Huber, Gary Koop, Luca Onorante

Research output: Contribution to journalArticle

2 Citations (Scopus)
9 Downloads (Pure)


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 shrinkage in such models. But the estimates produced by these priors can still have appreciable uncertainty. Sparsification has the potential to reduce 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 forecasting exercise, we find our approach to substantially improve forecast performance relative to shrinkage alone.
Original languageEnglish
Number of pages15
JournalJournal of Business and Economic Statistics
Early online date4 Feb 2020
Publication statusE-pub ahead of print - 4 Feb 2020


  • sparsity
  • shrinkage
  • hierarchical priors
  • time varying parameter regression

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