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Uma Nota sobre Erros de Previsão da Inflação de Curto Prazo

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  • Emanuel Kohlscheen

Abstract

This note shows that the unbiasedness and the weak rationality hypotheses are not rejected for the inflation forecasts surveyed by the Central Bank when the forecast horizon is one month. However, as in other countries, a clear pattern of auto-correlation of forecast errors is found. Furthermore, increases (decreases) in inflation are systematically associated with underestimations (overestimations) of inflation in the following month. This is true for both, the full sample of forecasters and the sample that is restricted to the 5 institutions with best forecasting performance, suggesting that models in which past realizations of inflation have greater weight in the formation of average expectations are more accurate than the assumption of rational expectations. Models aimed at explaining how expectations are formed should be able to explain these stylized facts as well as the hysteresis of forecasts.

Suggested Citation

  • Emanuel Kohlscheen, 2010. "Uma Nota sobre Erros de Previsão da Inflação de Curto Prazo," Working Papers Series 227, Central Bank of Brazil, Research Department.
  • Handle: RePEc:bcb:wpaper:227
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    File URL: http://www.bcb.gov.br/pec/wps/port/TD227.pdf
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    References listed on IDEAS

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    1. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    2. N. Gregory Mankiw & Ricardo Reis & Justin Wolfers, 2004. "Disagreement about Inflation Expectations," NBER Chapters,in: NBER Macroeconomics Annual 2003, Volume 18, pages 209-270 National Bureau of Economic Research, Inc.
    3. Martin Evans & Paul Wachtel, 1993. "Inflation regimes and the sources of inflation uncertainty," Proceedings, Federal Reserve Bank of Cleveland, pages 475-520.
    4. Kohlscheen, Emanuel, 2012. "Uma nota sobre erros de previsão da inflação de curto‐prazo," Revista Brasileira de Economia - RBE, FGV/EPGE - Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil), vol. 66(3), October.
    5. Steffen Henzel, 2008. "Learning Trend Inflation − Can Signal Extraction Explain Survey Forecasts?," ifo Working Paper Series 55, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    6. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    7. Cumby, Robert E & Huizinga, John, 1992. "Testing the Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressions," Econometrica, Econometric Society, vol. 60(1), pages 185-195, January.
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    Cited by:

    1. Wagner Piazza Gaglianone, 2017. "Empirical Findings on Inflation Expectations in Brazil: a survey," Working Papers Series 464, Central Bank of Brazil, Research Department.
    2. Daniela Kubudi & José Vicente, 2016. "A Joint Model of Nominal and Real Yield Curves," Working Papers Series 452, Central Bank of Brazil, Research Department.
    3. Tabak, Benjamin M. & Takami, Marcelo & Rocha, Jadson M.C. & Cajueiro, Daniel O. & Souza, Sergio R.S., 2014. "Directed clustering coefficient as a measure of systemic risk in complex banking networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 211-216.
    4. repec:fgv:epgrbe:v:66:n:3:a:2 is not listed on IDEAS
    5. repec:eee:intfor:v:33:y:2017:i:3:p:679-693 is not listed on IDEAS
    6. Kohlscheen, Emanuel, 2012. "Uma nota sobre erros de previsão da inflação de curto‐prazo," Revista Brasileira de Economia - RBE, FGV/EPGE - Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil), vol. 66(3), October.

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