Large time-varying parameter VARs

Gary Koop, Dimitris Korobilis

Research output: Contribution to journalArticle

93 Citations (Scopus)
72 Downloads (Pure)

Abstract

In this paper, we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints, we draw on ideas from the dynamic model averaging literature which achieve reductions in the computational burden through the use forgetting factors. We then extend the TVP-VAR so that its dimension can change over time. For instance, we can have a large TVP-VAR as the forecasting model at some points in time, but a smaller TVP-VAR at others. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output and interest rates demonstrates the feasibility and usefulness of our approach.
Original languageEnglish
Pages (from-to)185-198
Number of pages14
JournalJournal of Econometrics
Volume177
Issue number2
Early online date17 Apr 2013
DOIs
Publication statusPublished - Dec 2013

Keywords

  • parameter VARs
  • time-varying parameter vector autoregressive models
  • vector autoregressive models

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