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

Korobilis, Dimitris and Koop, Gary (2018) Variational Bayes inference in high-dimensional time-varying parameter models. Working Paper. Essex Finance Centre Working Papers, Colchester.

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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 variables election 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.

Item Type: Monograph (Working Paper)
Subjects: H Social Sciences > HG Finance
Divisions: Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Elements
Date Deposited: 17 Jul 2018 09:50
Last Modified: 07 Aug 2019 21:15
URI: http://repository.essex.ac.uk/id/eprint/22665

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