Munich Personal RePEc Archive

Variational Bayes inference in high-dimensional time-varying parameter models

Koop, Gary and Korobilis, Dimitris (2018): Variational Bayes inference in high-dimensional time-varying parameter models.

[img]
Preview
PDF
MPRA_paper_87972.pdf

Download (644kB) | Preview

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.

UB_LMU-Logo
MPRA is a RePEc service hosted by
the Munich University Library in Germany.