 Bayesian model selection for unit root testing with multiple structural breaksAlexander Vosseler Computational Statistics & Data Analysis, 2016, vol. 100, issue C, 616-630 Abstract: A fully Bayesian approach to unit root testing with multiple structural breaks is presented. For this purpose the number of breaks, the corresponding break dates as well as the number of autoregressive lags are treated as model indicators, whose posterior distributions are explored using a hybrid Markov chain Monte Carlo sampling strategy. The performance of the sampling algorithm is demonstrated on the basis of several Monte Carlo experiments. In a next step the most likely model is used to test for a unit root with possible multiple breaks by computing the posterior probability of this point hypothesis under different prior distributions. The sensitivity of the test results with regard to the assumed prior distribution is analyzed and the Bayes test is compared with some classical unit root tests by means of power functions. Finally, in an empirical application the yearly unemployment rates of 17 OECD countries are analyzed to answer the question if there is persistence after a labor market shock. Keywords: Bayesian model selection; Markov chain Monte Carlo; Multiple structural breaks; OECD unemployment rates; Unit root test (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:100:y:2016:i:c:p:616-630 DOI: 10.1016/j.csda.2014.08.016 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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