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Uma Nota sobre Erros de Previsão da Inflação de Curto Prazo

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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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    1. 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.
    2. N. Gregory Mankiw & Ricardo Reis, 2002. "Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(4), pages 1295-1328.
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    5. 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.
    6. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    7. Kohlscheen, Emanuel, 2012. "Uma nota sobre erros de previsão da inflação de curto-prazo," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 66(3), October.
    8. 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.
    9. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
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    Cited by:

    1. Daniela Kubudi & José Valentim Vicente, 2016. "A Joint Model of Nominal and Real Yield Curves," Working Papers Series 452, Central Bank of Brazil, Research Department.
    2. Cambara, Leilane de Freitas Rocha & Meurer, Roberto & Lima, Gilberto Tadeu, 2022. "Deviating from full rationality but not from theoretical consistency: The behavior of inflation expectations in Brazil," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 492-501.
    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. Garcia, Márcio G.P. & Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2017. "Real-time inflation forecasting with high-dimensional models: The case of Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 679-693.
    6. Kohlscheen, Emanuel, 2012. "Uma nota sobre erros de previsão da inflação de curto-prazo," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 66(3), October.
    7. Marta Baltar Moreira Areosa & Wagner Piazza Gaglianone, 2023. "Anchoring Long-term VAR Forecasts Based On Survey Data and State-space Models," Working Papers Series 574, Central Bank of Brazil, Research Department.
    8. 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).
    9. Leilane de Freitas Rocha Cambara & Roberto Meurer, Gilberto Tadeu Lima, 2019. "Deviating from Perfect Foresight but not from Theoretical Consistency: The Behavior of Inflation Expectations in Brazil," Working Papers, Department of Economics 2019_36, University of São Paulo (FEA-USP).
    10. Carlos Henrique Dias Cordeiro de Castro & Fernando Antonio Lucena Aiube, 2023. "Forecasting inflation time series using score‐driven dynamic models and combination methods: The case of Brazil," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 369-401, March.

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