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Bayes model averaging of cyclical decompositions in economic time series
Author(s) -
Kleijn Richard,
van Dijk Herman K.
Publication year - 2006
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.823
Subject(s) - markov chain monte carlo , series (stratigraphy) , bayes' theorem , econometrics , computer science , monte carlo method , markov chain , state space representation , convergence (economics) , bayesian probability , mathematics , statistics , algorithm , economics , paleontology , biology , economic growth
A flexible decomposition of a time series into stochastic cycles under possible non‐stationarity is specified, providing both a useful data analysis tool and a very wide model class. A Bayes procedure using Markov Chain Monte Carlo (MCMC) is introduced with a model averaging approach which explicitly deals with the uncertainty on the appropriate number of cycles. The convergence of the MCMC method is substantially accelerated through a convenient reparametrization based on a hierarchical structure of variances in a state space model. The model and corresponding inferential procedure are applied to simulated data and to cyclical economic time series like US industrial production and unemployment. We derive the implied posterior distributions of model parameters and some relevant functions thereof, shedding light on several key features of economic time series. Copyright © 2006 John Wiley & Sons, Ltd.