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Forecasting Brazilian output in the presence of breaks: a comparison of linear and nonlinear models

Author

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  • Marcelle Chauvet
  • Elcyon C. R. Lima
  • Brisne Vasquez

Abstract

This paper compares the forecasting performance of linear and nonlinear models under the presence of structural breaks for the Brazilian real GDP growth. The Markov-switching models proposed by Hamilton (1989) and its generalized version proposed by Lam (1991) are applied to quarterly GDP from 1975:1 to 2000:2 allowing for breaks at the Collor Plans. The probabilities of recessions are used to analyze the Brazilian business cycle. The ability of each model in forecasting out-of-sample the growth rates of GDP is examined. The forecasting ability of the two models is also compared with linear specifications. The authors find that nonlinear models display the best forecasting performance and that specifications including the presence of structural breaks are important in obtaining a representation of the Brazilian business cycle.

Suggested Citation

  • Marcelle Chauvet & Elcyon C. R. Lima & Brisne Vasquez, 2002. "Forecasting Brazilian output in the presence of breaks: a comparison of linear and nonlinear models," FRB Atlanta Working Paper 2002-28, Federal Reserve Bank of Atlanta.
    • Handle: RePEc:fip:fedawp:2002-28
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    References listed on IDEAS

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    1. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    2. Chauvet, Marcelle, 2002. "The Brazilian Business and Growth Cycles," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 56(1), January.
    3. Maximo Camacho & Gabriel Perez-Quiros, 2002. "This is what the leading indicators lead," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(1), pages 61-80.
    4. Albert, James H & Chib, Siddhartha, 1993. "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 1-15, January.
    5. Hess, Gregory D & Iwata, Shigeru, 1997. "Measuring and Comparing Business-Cycle Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 432-444, October.
    6. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    7. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
    8. Don Harding & Adrian Pagan, 1999. "Knowing the Cycle," Melbourne Institute Working Paper Series wp1999n12, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    9. Pablo Mejía-Reyes, 1999. "Classical business cycles in Latin America: Turning points, asimmetries and international synchronisation," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 14(2), pages 265-297.
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    Cited by:

    1. repec:fgv:epgrbe:v:67:n:1:a:4 is not listed on IDEAS
    2. Roberto Tatiwa Ferreira & Herman Bierens & Ivan Castelar, 2005. "Forecasting Quarterly Brazilian GDP Growth Rate With Linear and NonLinear Diffusion Index Models," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 6(3), pages 261-292.
    3. Igor Alexandre Clemente de Morais & Marcelo Savino Portugal, 2003. "Business Cycle in the Industrial Production of Brazilian States," Anais do XXXI Encontro Nacional de Economia [Proceedings of the 31st Brazilian Economics Meeting] e75, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    4. Patrick T. Kanda & Mehmet Balcilar & Pejman Bahramian & Rangan Gupta, 2016. "Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation," Applied Economics, Taylor & Francis Journals, vol. 48(26), pages 2412-2427, June.
    5. M. Portugal & I.A. de Morais, 2004. "STRUCTURAL CHANGE IN THE BRAZILIAN DEMAND FOR IMPORTS: A regime switching approach," Econometric Society 2004 Latin American Meetings 346, Econometric Society.

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    Keywords

    Forecasting; Economic conditions - Brazil; Econometric models; Brazil;
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