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Studies in Business Cycles Using Markov-switching Models

Abstract : The severity of the Great Recession has renewed interest in the analysis of business cycles. Our thesis pertains to this revival of attention for the study of cyclical fluctuations. After reviewing the regime-switching models in Chapter one, the following chapter suggests a chronology of the classical business cycle in French economy for the 1970-2009 period. To that end, three dating methodologies are used: the rule of thumb of two consecutive quarters of negative growth, the non-parametric approach of Bry and Boschan (1971), and the Markov-switching approach of Hamilton (1989). The results show that, omitted structural breaks may hinder the Markov-switching approach to capture business-cycle fluctuations. However, when such breaks are allowed for, the timing of the French recessions provided by the Markov-switching model closely matches those derived by the rule-based approaches. Chapter 3 performs a nonlinearity analysis inMarkov-switching modelling using a set of non-standard tests. Monte Carlo analysis reveals that a recently test proposed by Carrasco, Hu, and Ploberger (2013) for Markov switching has low power for empirically-relevant data generating processes when allowing for serial correlation under the null. By contrast, a parametric bootstrap likelihood ratio (LR) test of Markov switching has higher power in the same setting, providing stronger support for nonlinearity in quarterly French and U.S. real GDP. When testing for Markov switching in mean or intercept of an autoregressive process, it is important to allow for serial correlation under the null hypothesis of linearity. Otherwise, a rejection of linearity could merely reflect misspecification of the persistence properties of the data, rather than any inherent nonlinearity. Chapter 4 examines whether controlling for structural breaks improves the forecasting performance of the Markov-switching models, as compared to their linear counterparts. The approach considered to answer this issue is to combined forecasts across different estimation windows. The outcome of applying such an approach shows that, including data from periods preceding structural breaks and particularly the "Great Moderation" improves upon forecasts based on data drawn exclusively from these episodes. Accordingly, Markov-switching models forecast the probability of events such as the Great Recession more accurately than their linear counterparts. The general conclusions summarize the main results of the thesis and, suggest several directions for future research.
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Submitted on : Thursday, March 29, 2018 - 12:58:18 PM
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Zohra Rabah - Romdhane. Studies in Business Cycles Using Markov-switching Models. Economies et finances. Université de Lorraine, 2013. Français. ⟨NNT : 2013LORR0322⟩. ⟨tel-01750920⟩



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