Duration-dependent Markov-switching model: an empirical study for the Brazilian business cycle
Fernando Mendes (),
JoÃ£o Caldeira () and
Guilherme Moura ()
Additional contact information
Fernando Mendes: Federal University of Rio Grande do Sul
JoÃ£o Caldeira: Federal University of Rio Grande do Sul
Guilherme Moura: Federal University of Santa Catarina
Economics Bulletin, 2019, vol. 39, issue 1, 676-685
This paper uses a duration-dependent Markov-switching model to identify business cycles in the Brazilian economy and to test for the presence of duration dependence in periods of expansion and contraction. The model is estimated using the growth rate of quarterly GDP from 1980:II to 2016:II. In the empirical application we found evidence of significant asymmetry in growth rates and duration dependence in the business cycle transition probabilities. The parameter estimates indicated that as the recession ages, the probability of a transition into an expansion increases (positive duration dependence in recessions). On the other hand, as the expansions ages, the probability of a transition into a recession decreases (negative duration dependence in expansions). The smoothed probabilities of the model captured several periods of contraction during the last three decades, matching the recession dates of the Business Cycle Dating Committee (CODACE) from the GetÃºlio Vargas Foundation.
Keywords: Business Cycles; Markov-switching model; Duration Dependence (search for similar items in EconPapers)
JEL-codes: E3 N1 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ebl:ecbull:eb-18-00262
Access Statistics for this article
More articles in Economics Bulletin from AccessEcon
Bibliographic data for series maintained by John P. Conley ().