Forecasting with high dimensional panel VARs

Gary Koop, Dimitris Korobilis

Research output: Contribution to journalArticle

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
Number of pages44
JournalOxford Bulletin of Economics and Statistics
Publication statusAccepted/In press - 2 Oct 2018

Keywords

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

Fingerprint Dive into the research topics of 'Forecasting with high dimensional panel VARs'. Together they form a unique fingerprint.

  • Profiles

    No photo of Gary Koop

    Cite this