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Coincident and Leading Indicators for Brazilian Cycles

In: Business Cycles in BRICS

Author

Listed:
  • Aloisio Campelo

    (FGV/IBRE)

  • Ataman Ozyildirim

    (The Conference Board Inc.)

  • Jing Sima-Friedman

    (The Conference Board Inc.)

  • Paulo Picchetti

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

  • Sarah Piassi Machado Lima

    (FGV/IBRE)

Abstract

In this chapter, we develop coincident and leading indexes of economic activity for Brazil following the approach used by The Conference Board (TCB). We show that our selection of coincident indicators and the composite index created from them closely follow the Brazilian Business Cycle Dating Committee (CODACE) chronology. Using the coincident index as the target variable, we select a small set of leading indicators and show that the composite index of these indicators help predict turning points in the business cycle better than its individual components.

Suggested Citation

  • Aloisio Campelo & Ataman Ozyildirim & Jing Sima-Friedman & Paulo Picchetti & Sarah Piassi Machado Lima, 2019. "Coincident and Leading Indicators for Brazilian Cycles," Societies and Political Orders in Transition, in: Sergey Smirnov & Ataman Ozyildirim & Paulo Picchetti (ed.), Business Cycles in BRICS, pages 315-329, Springer.
    • Handle: RePEc:spr:socchp:978-3-319-90017-9_18
    DOI: 10.1007/978-3-319-90017-9_18
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    More about this item

    Keywords

    Business cycle; Turning points; Indicators; Coincident index; Leading index; Time series; Markov switching/forecasting;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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