Large Time-Varying Parameter VARs

36 Pages Posted: 18 Mar 2012

See all articles by Dimitris Korobilis

Dimitris Korobilis

University of Glasgow - Adam Smith Business School

Gary Koop

University of Strathclyde, Glasgow - Strathclyde Business School - Department of Economics

Date Written: March 1, 2012

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 with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. 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.

Keywords: Bayesian VAR, forecasting, time-varying coefficients, state-space model

JEL Classification: C11, C52, E27, E37

Suggested Citation

Korobilis, Dimitris and Koop, Gary, Large Time-Varying Parameter VARs (March 1, 2012). Available at SSRN: https://ssrn.com/abstract=2025216 or http://dx.doi.org/10.2139/ssrn.2025216

Dimitris Korobilis (Contact Author)

University of Glasgow - Adam Smith Business School ( email )

40 University Avenue
Gilbert Scott Building
Glasgow, Scotland G12 8QQ
United Kingdom

HOME PAGE: http://https://sites.google.com/site/dimitriskorobilis/

Gary Koop

University of Strathclyde, Glasgow - Strathclyde Business School - Department of Economics ( email )

100 Cathedral Street
Glasgow G4 0LN
United Kingdom

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