Variational Bayes Inference in High-Dimensional Time-Varying Parameter Models

60 Pages Posted: 28 Sep 2018

See all articles by Gary Koop

Gary Koop

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

Dimitris Korobilis

University of Glasgow - Adam Smith Business School

Date Written: July 15, 2018

Abstract

This paper proposes a mean field variational Bayes algorithm for efficient posterior and predictive inference in time-varying parameter models. Our approach involves: i) computationally trivial Kalman filter updates of regression coefficients, ii) a dynamic variable selection prior that removes irrelevant variables in each time period, and iii) a fast approximate state-space estimator of the regression volatility parameter. In an exercise involving simulated data we evaluate the new algorithm numerically and establish its computational advantages. Using macroeconomic data for the US we find that regression models that combine time-varying parameters with the information in many predictors have the potential to improve forecasts over a number of alternatives.

Keywords: dynamic linear model, approximate posterior inference, dynamic variable selection, forecasting

JEL Classification: C11, C13, C52, C53, C61

Suggested Citation

Koop, Gary and Korobilis, Dimitris, Variational Bayes Inference in High-Dimensional Time-Varying Parameter Models (July 15, 2018). Available at SSRN: https://ssrn.com/abstract=3246472 or http://dx.doi.org/10.2139/ssrn.3246472

Gary Koop

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

100 Cathedral Street
Glasgow G4 0LN
United Kingdom

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/

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