Bayes factors and nonlinearity: Evidence from economic time series
type: Item
instance of: scholarly article
title: Bayes factors and nonlinearity: Evidence from economic time series
zbMATH Open document ID: 0937.62124
DOI: 10.1016/S0304-4076(98)00031-1
author: Gary Koop
author: Simon Potter
published in: Journal of Econometrics
publication date: 2000-06-14T00:00:00Z
review text: This paper argues in favor of a Bayesian approach to evaluating evidence of nonlinearity in economic time series over the classical approach that has been dominant in the applied literature. An application is presented concerning nonlinearity in US GNP. The outline of the rest of the paper is as follows. Section 2 gives some formal definitions of Bayes factors and outlines the issue of prior sensitivity for a general class of nonlinear time series models. Section 3 discusses the advantages of a Bayesian approach to nonlinear time series modeling. Section 4 contrasts Bayesian methods to classical ones for a simple threshold model. Section 5 discusses various computational techniques for calculating marginal likelihoods and Bayes factors. Section 6 discusses the formulation of priors and carries out an empirical exercise where the Bayes factors for two nonlinear time series models of US GNP are calculated. The last section concludes.
Mathematics Subject Classification ID: 62P20
Mathematics Subject Classification ID: 91B84
Mathematics Subject Classification ID: 65C40
zbMATH DE Number: 1342892
zbMATH Keywords: Markov chain Monte Carlo
zbMATH Keywords: Markov trend model
zbMATH Keywords: threshold autoregressive model
zbMATH Keywords: Bayes factors
MaRDI profile type: Publication
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full work available at URL: s0304-4076(98)00031-1
OpenAlex ID: W1976028601
description: scientific article; zbMATH DE number 1342892