Template-Type: ReDIF-Paper 1.0
Author-Name: Giordani, P.
Author-Name-Last: Giordani
Author-Name-First: Paolo
Author-Name: Kohn, R.
Author-Name-Last: Kohn
Author-Name-First: Robert
Author-Name: van Dijk, D.J.C.
Author-Name-Last: van Dijk
Author-Name-First: Dick
Author-Person: pva27
Title: A unified approach to nonlinearity, structural change and outliers
Abstract: This paper demonstrates that the class of conditionally linear and Gaussian state-space models offers a general and convenient framework for simultaneously handling nonlinearity, structural change and outliers in time series. Many popular nonlinear time series models, including threshold, smooth transition and Markov-Switching models, can be written in state-space form. It is then straightforward to add components that capture parameter instability and intervention effects. We advocate a Bayesian approach to estimation and inference, using an efficient implementation of Markov Chain Monte Carlo sampling schemes for such linear dynamic mixture models. The general modelling framework and the Bayesian methodology are illustrated by means of several examples. An application to quarterly industrial production growth rates for the G7 countries demonstrates the empirical usefulness of the approach.
Creation-Date: 2005-03-09
File-URL: https://repub.eur.nl/pub/1910/ei200509.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2005-09
Classification-JEL: C11, C22, E32
Keywords: Bayesian inference, Markov-switching models, business cycle asymmetry, state-space models, threshold models
Handle: RePEc:ems:eureir:1910