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Structural Trends and Cycles in a DSGE Model for Brazil

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  • Silvio Michael de Azevedo Costa

Abstract

This paper builds and estimates a structural growth model with micro-founded specifications of trends and cycles to allow a consistent multivariate filtering of key macroeconomic variables. Emerging countries like Brazil show expressive trend dynamics that can blur the vectors determining real business cycles, but most models cannot consider this additional complexity, and so they usually throw out meaningful dynamics of scarce data. Our basic DSGE model cares for the raw data dynamics and aims to disentangle endogenously trends and cycles and unveil the underlying growth vectors. Thereby, historical interpretation and forecasts can hold internal consistency, which improves storytelling and calls for an in-depth forecasting. We report model evaluation summaries to support fully integrated models as a highly valuable tool for applied macro exercises and policy advising. Yet, we present an application for the measurement of potential output and the output gap.

Suggested Citation

  • Silvio Michael de Azevedo Costa, 2016. "Structural Trends and Cycles in a DSGE Model for Brazil," Working Papers Series 434, Central Bank of Brazil, Research Department.
  • Handle: RePEc:bcb:wpaper:434
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    References listed on IDEAS

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    Cited by:

    1. Leonid A. Serkov, 2018. "The External and Internal Shocks' Impact on Macroeconomic Indicators of a Region," Journal of New Economy, Ural State University of Economics, vol. 19(4), pages 45-63, August.
    2. Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023. "Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).
    3. Angelo Marsiglia Fasolo & Eurilton Araújo & Marcos Valli Jorge & Alexandre Kornelius & Leonardo Sousa Gomes Marinho, 2023. "Brazilian Macroeconomic Dynamics Redux: Shocks, Frictions, and Unemployment in SAMBA Model," Working Papers Series 578, Central Bank of Brazil, Research Department.

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