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Analyzing COPOM’s Decisions

Author

Listed:
  • Paulo Rogério Faustino Matos

    (Federal University of Ceara)

  • Jayme Andrade Neto

    (Federal Institute Sul-Rio-Grandense)

Abstract

This paper aims at modeling the conduct policy of the interest rate by the Monetary Policy Committee of the Central Bank of Brazil (COPOM), following methodologically a statistical framework developed by Engle & Russel (1998) and used by Hamilton & Jordà (2002) in studying the decisions of the Federal Reserve of the United States. The results, once we observe the period from January 2002 to July 2010 with a weekly frequency, suggest that COPOM is influenced by government expenditures and that it has a forward-looking behavior, holding onto the expectations of the Gross Domestic Product (GDP) and the official inflation of the country. In addition to this relevant evidence in the discussion of the balance between fiscal and monetary policies in Brazil, Brazilian monetary authority theoretically possesses a behavior which is aligned to that observed in practice, though it presents a greater sensitivity to macroeconomic variations.

Suggested Citation

  • Paulo Rogério Faustino Matos & Jayme Andrade Neto, 2015. "Analyzing COPOM’s Decisions," Brazilian Business Review, Fucape Business School, vol. 12(6), pages 24-47, November.
    • Handle: RePEc:bbz:fcpbbr:v:12:y:2015:i:6:p24-47
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    References listed on IDEAS

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    1. Bonomo, Marco Antonio Cesar & Carrasco, Vinícius & Moreira, Humberto, 2003. "Aprendizado Evolucionário, Inércia Inflacionária e Recessão em Desinflações Monetárias," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 57(4), October.
    2. James D. Hamilton & Oscar Jorda, 2002. "A Model of the Federal Funds Rate Target," Journal of Political Economy, University of Chicago Press, vol. 110(5), pages 1135-1167, October.
    3. Teles, Vladimir Kühl & Nemoto, Joana, 2005. "O Regime de Metas de Inflação do Brasil é Crível?," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 59(3), July.
    4. Lee, Sang-Won & Hansen, Bruce E., 1994. "Asymptotic Theory for the Garch(1,1) Quasi-Maximum Likelihood Estimator," Econometric Theory, Cambridge University Press, vol. 10(1), pages 29-52, March.
    5. repec:bla:jecsur:v:22:y:2008:i:4:p:711-751 is not listed on IDEAS
    6. Aragón, Edilean Kleber da Silva Bejarano & Portugal, Marcelo Savino, 2009. "Central Bank preferences and monetary rules under the inflation targeting regime in Brasil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 29(1), May.
    7. Engle, Robert F. & Russell, Jeffrey R., 1997. "Forecasting the frequency of changes in quoted foreign exchange prices with the autoregressive conditional duration model," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 187-212, June.
    8. Robert F. Engle, 2000. "The Econometrics of Ultra-High Frequency Data," Econometrica, Econometric Society, vol. 68(1), pages 1-22, January.
    9. Lumsdaine, Robin L, 1996. "Consistency and Asymptotic Normality of the Quasi-maximum Likelihood Estimator in IGARCH(1,1) and Covariance Stationary GARCH(1,1) Models," Econometrica, Econometric Society, vol. 64(3), pages 575-596, May.
    10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    11. Bonomo, Marco Antonio Cesar & Brito, Ricardo D., 2002. "Regras Monetárias e Dinâmica Macroeconômica no Brasil: Uma Abordagem de Expectativas Racionais," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 56(4), October.
    12. Pastore, Affonso Celso & Pinotti, Maria Cristina, 1999. "Inflação e Estabilização: Algumas Lições da Experiência Brasileira," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 53(1), January.
    13. Alexandre Batista Ferreira & Frederico Gonzaga Jayme Júnior, 2005. "Metas De Inflação E Vulnerabilidade Externa No Brasil," Anais do XXXIII Encontro Nacional de Economia [Proceedings of the 33rd Brazilian Economics Meeting] 035, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
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