IDEAS home Printed from https://ideas.repec.org/p/spa/wpaper/2019wpecon36.html
   My bibliography  Save this paper

Deviating from Perfect Foresight but not from Theoretical Consistency: The Behavior of Inflation Expectations in Brazil

Author

Listed:
  • Leilane de Freitas Rocha Cambara
  • Roberto Meurer, Gilberto Tadeu Lima

Abstract

The aim of this paper is to investigate whether inflation expectations in Brazil have characteristics and statistical properties that can be correlated (possibly in a causal way) with observed variables of interest and expectations about them. We test the hypothesis of perfect foresight in the formation of inflation expectations by the respondents of the official survey conducted by the Central Bank of Brazil, examining the behavior of the possible forecast errors. As these errors are biased and can be predicted, we reject the hypothesis of perfect foresight. We also test models of noisy and sticky information, and we cannot conclude that the deviations from perfect foresight can be explained by information rigidity. Additionally, with a Vector Error Correction model, we find evidence that the expectations about the related macroeconomic variables respond to each other as predicted by a theoretically-grounded macroeconomic model. Therefore, inflation expectations in Brazil are to an important extent consistent with more general expectations about the future performance of the economy.

Suggested Citation

  • 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).
  • Handle: RePEc:spa:wpaper:2019wpecon36
    as

    Download full text from publisher

    File URL: http://www.repec.eae.fea.usp.br/documentos/Cambara_Meurer_Lima_36WP.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. repec:fgv:epgrbe:v:66:n:3:a:2 is not listed on IDEAS
    2. Bank for International Settlements, 2010. "Monetary policy and the measurement of inflation: prices, wages and expectations," BIS Papers, Bank for International Settlements, number 49.
    3. 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.
    4. Castro, Marcos R. de & Gouvea, Solange N. & Minella, Andre & Santos, Rafael & Souza-Sobrinho, Nelson F., 2015. "SAMBA: Stochastic Analytical Model with a Bayesian Approach," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 35(2), March.
    5. Olivier Coibion & Yuriy Gorodnichenko, 2015. "Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts," American Economic Review, American Economic Association, vol. 105(8), pages 2644-2678, August.
    6. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    7. Arnildo da Silva Correa & Paulo Picchetti, 2016. "New Information and Updating of Market Experts’ Inflation Expectations," Working Papers Series 411, Central Bank of Brazil, Research Department.
    8. 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.
    9. Minella, Andre & de Freitas, Paulo Springer & Goldfajn, Ilan & Muinhos, Marcelo Kfoury, 2003. "Inflation targeting in Brazil: constructing credibility under exchange rate volatility," Journal of International Money and Finance, Elsevier, vol. 22(7), pages 1015-1040, December.
    10. Mark Gertler & Kenneth Rogoff (ed.), 2004. "NBER Macroeconomics Annual 2003," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262572214, April.
    11. Berge, Travis J., 2018. "Understanding survey-based inflation expectations," International Journal of Forecasting, Elsevier, vol. 34(4), pages 788-801.
    12. Edwards, Sebastian, 2003. "Financial instability in Latin America," Journal of International Money and Finance, Elsevier, vol. 22(7), pages 1095-1106, December.
    13. Carlos Hamilton V Araujo & Wagner P Gaglianone, 2010. "Survey-based inflation expectations in Brazil," BIS Papers chapters, in: Bank for International Settlements (ed.), Monetary policy and the measurement of inflation: prices, wages and expectations, volume 49, pages 107-114, Bank for International Settlements.
    14. Jacob A. Mincer, 1969. "Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance," NBER Books, National Bureau of Economic Research, Inc, number minc69-1.
    15. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Wagner Piazza Gaglianone, 2017. "Empirical Findings on Inflation Expectations in Brazil: a survey," Working Papers Series 464, Central Bank of Brazil, Research Department.
    3. Florian Peters & Simas Kucinskas, 2018. "Measuring Biases in Expectation Formation," Tinbergen Institute Discussion Papers 18-058/IV, Tinbergen Institute.
    4. Gaglianone, Wagner Piazza & Giacomini, Raffaella & Issler, João Victor & Skreta, Vasiliki, 2022. "Incentive-driven inattention," Journal of Econometrics, Elsevier, vol. 231(1), pages 188-212.
    5. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    6. Michael P. Clements, 2022. "Forecaster Efficiency, Accuracy, and Disagreement: Evidence Using Individual‐Level Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(2-3), pages 537-568, March.
    7. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    8. Berge, Travis J., 2018. "Understanding survey-based inflation expectations," International Journal of Forecasting, Elsevier, vol. 34(4), pages 788-801.
    9. Clements, Michael P., 2019. "Do forecasters target first or later releases of national accounts data?," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1240-1249.
    10. Boskabadi, Elahe, 2022. "Economic policy uncertainty and forecast bias in the survey of professional forecasters," MPRA Paper 115081, University Library of Munich, Germany.
    11. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    12. An, Zidong & Zheng, Xinye, 2023. "Diligent forecasters can make accurate predictions despite disagreeing with the consensus," Economic Modelling, Elsevier, vol. 125(C).
    13. Sushant Acharya, 2017. "Costly Information, Planning Complementarities, and the Phillips Curve," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(4), pages 823-850, June.
    14. Cornand, Camille & Hubert, Paul, 2020. "On the external validity of experimental inflation forecasts: A comparison with five categories of field expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
    15. repec:zbw:bofrdp:037 is not listed on IDEAS
    16. Thomas Lustenberger & Enzo Rossi, 2022. "The Social Value of Information: A Test of a Beauty and Nonbeauty Contest," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(7), pages 2125-2148, October.
    17. James M. Nason & Gregor W. Smith, 2021. "Measuring the slowly evolving trend in US inflation with professional forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 1-17, January.
    18. Gerunov, Anton, 2014. "Критичен Преглед На Основните Подходи За Моделиране На Икономическите Очаквания [A Critical Review of Major Approaches for Modeling Economic Expectations]," MPRA Paper 68797, University Library of Munich, Germany.
    19. Kim, Insu & Kim, Young Se, 2019. "Inattentive agents and inflation forecast error dynamics: A Bayesian DSGE approach," Journal of Macroeconomics, Elsevier, vol. 62(C).
    20. Tomasz Łyziak & Xuguang Simon Sheng, 2023. "Disagreement in Consumer Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(8), pages 2215-2241, December.
    21. Yasutomo Murasawa, 2020. "Measuring public inflation perceptions and expectations in the UK," Empirical Economics, Springer, vol. 59(1), pages 315-344, July.

    More about this item

    Keywords

    Inflation expectations in Brazil; forecast errors in surveys; deviations from perfect foresight;
    All these keywords.

    JEL classification:

    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spa:wpaper:2019wpecon36. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pedro Garcia Duarte (email available below). General contact details of provider: https://edirc.repec.org/data/deuspbr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.