A Bayesian Approach to Predicting Cycles Using Composite Indicators

Paulo Picchetti ()
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Paulo Picchetti: Fundação Getulio Vargas, Brazil/IBRE/São Paulo School of Economics

A chapter in Business Cycles in BRICS, 2019, pp 337-345 from Springer

Abstract: 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.

Keywords: C32; C42; C53; E32; Composite leading and coincident indicators; Recessions; Forecasting (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:socchp:978-3-319-90017-9_20

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DOI: 10.1007/978-3-319-90017-9_20

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