Abstract
This paper proposes a methodology for estimating the probabilities of recession starts and endings in the Brazilian economy. The model providing these estimations is a logistic regression using as covariates some transformations of composite leading and coincident indicators for Brazilian economic cycles. A very attractive feature of this approach is the avoidance of the need for extrapolating the information beyond the available sample, allowing for more reliable real-time assessments. It is shown that a Bayesian approach to the estimation of the model produces more robust and interpretable results.
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Picchetti, P. (2019). A Bayesian Approach to Predicting Cycles Using Composite Indicators. In: Smirnov, S., Ozyildirim, A., Picchetti, P. (eds) Business Cycles in BRICS. Societies and Political Orders in Transition. Springer, Cham. https://doi.org/10.1007/978-3-319-90017-9_20
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DOI: https://doi.org/10.1007/978-3-319-90017-9_20
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