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A Bayesian Approach to Predicting Cycles Using Composite Indicators

In: Business Cycles in BRICS

Author

Listed:
  • Paulo Picchetti

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

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.

Suggested Citation

  • Paulo Picchetti, 2019. "A Bayesian Approach to Predicting Cycles Using Composite Indicators," Societies and Political Orders in Transition, in: Sergey Smirnov & Ataman Ozyildirim & Paulo Picchetti (ed.), Business Cycles in BRICS, pages 337-345, Springer.
    • Handle: RePEc:spr:socchp:978-3-319-90017-9_20
    DOI: 10.1007/978-3-319-90017-9_20
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    More about this item

    Keywords

    C32; C42; C53; E32;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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