Forecasting with high dimensional panel VARs

Gary Koop, Dimitris Korobilis

Research output: Contribution to journalArticlepeer-review

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Abstract

This paper develops methods for estimating and forecasting in Bayesian panel vector autoregressions of large dimensions with time-varying parameters and stochastic volatility. We exploit a hierarchical prior that takes into account possible pooling restrictions involving both VAR coefficients and the error covariance matrix, and propose a Bayesian dynamic learning procedure that controls for various sources of model uncertainty. We tackle computational concerns by means of a simulation-free algorithm that relies on an analytical approximation of the posterior distribution. We use our methods to forecast inflation rates in the eurozone and show that forecasts from our flexible specification are superior to alternative methods for large vector autoregressions.
Original languageEnglish
Pages (from-to)937-959
Number of pages23
JournalOxford Bulletin of Economics and Statistics
Volume81
Issue number5
Early online date28 Feb 2019
DOIs
Publication statusPublished - 1 Oct 2019

Keywords

  • panel VAR
  • inflation forecasting
  • Bayesian
  • time-varying parameter model

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