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• W2550625190 abstract "Markov switching dynamic models provide a good tool of describing asymmetric business cycle. However, deciding the adequacy of the model’s fit to the data requires statistical testing procedures. Testing for nonlinearity of Markov switching models is problematic due to the presence of unidentified nuisance parameters under the null of linearity and identical zero scores. This paper revisits the problem of testing for nonlinearity in this context. It investigates the dynamics of the French business cycle using a two-state Markov switching autoregressive model. In order to more capture the cyclical fluctuations of the French Gross Domestic Product (GDP) growth, I allow for a structural break in mean. And to make a more formal assessment of the performance of the model, I carry out formal hypothesis tests of nonlinearity. The results imply significant uncertainty regarding the statistical significance of the model fitted to French data. Given this uncertainty, I investigate the finite sample performance of the tests of interest to derive trustworthy conclusions. The Monte Carlo results show that the bootstrap test outperforms the Garcia asymptotic test and the Carrasco, Hu, and Ploberger test. The latter is not informative when considering a Markov switching mean alternative. In the context of Markov switching models where asymptotic theory is difficult to achieve, the bootstrap testing may be very attractive." @default.
• W2550625190 created "2016-11-30" @default.
• W2550625190 creator A5069219785 @default.
• W2550625190 date "2017-01-01" @default.
• W2550625190 modified "2023-10-09" @default.
• W2550625190 title "Markov-Switching Models" @default.
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• W2550625190 doi "https://doi.org/10.7551/mitpress/6444.003.0006" @default.
• W2550625190 hasPublicationYear "2017" @default.
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