Inducing sparsity and shrinkage in time-varying parameter models

Florian Huber, Gary Koop, Luca Onorante

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
17 Downloads (Pure)

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 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 article, 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
Pages (from-to)669-683
Number of pages15
JournalJournal of Business and Economic Statistics
Volume39
Issue number3
Early online date4 Feb 2020
DOIs
Publication statusPublished - 3 Jul 2021

Keywords

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

Cite this