Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models

Niko Hauzenberger, Florian Huber, Gary Koop, Luca Onorante

Research output: Contribution to journalArticle

Abstract

In this paper, we write the time-varying parameter regression model involving K explanatory variables and T observations as a constant coefficient regression model with TK explanatory variables. In contrast with much of the existing literature which assumes coefficients to evolve according to a random walk, this specification does not restrict the form that the time-variation in coefficients can take. We develop computationally efficient Bayesian econometric methods based on the singular value decomposition of the TK regressors. In artificial data, we find our methods to be accurate and much faster than standard approaches in terms of computation time. In an empirical exercise involving inflation forecasting using a large number of predictors, we find our methods to forecast better than alternative approaches and document different patterns of parameter change than are found with approaches which assume random walk evolution of parameters.
Original languageEnglish
JournalarXiv
Publication statusPublished - 23 Oct 2019

Fields of Science and Technology Classification 2012

  • 502 Economics

Keywords

  • econ.EM
  • stat.CO

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