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Understanding survey-based inflation expectations

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  • Berge, Travis J.

Abstract

This paper examines the behavior of inflation expectations in the United States. After documenting deviations from rationality in survey-based inflation expectations, I apply a model selection algorithm, boosting, to the inflation expectations of households and professionals. The algorithm builds a regression-like model of expected inflation using a large panel of macroeconomic data as possible covariates. The algorithm achieves a very strong fit in-sample, and finds that the inflation expectations of households correlate with different macroeconomic variables from the expectations of professionals. However, it is difficult to exploit the predictability of inflation expectations in order to improve forecasts of the realized inflation.

Suggested Citation

  • Berge, Travis J., 2018. "Understanding survey-based inflation expectations," International Journal of Forecasting, Elsevier, vol. 34(4), pages 788-801.
    • Handle: RePEc:eee:intfor:v:34:y:2018:i:4:p:788-801
    DOI: 10.1016/j.ijforecast.2018.07.003
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    Cited by:

    1. Bernd Hayo & Florian Neumeier, 2022. "Households’ inflation perceptions and expectations: survey evidence from New Zealand," International Economics and Economic Policy, Springer, vol. 19(1), pages 185-217, February.
    2. 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).
    3. Randal J. Verbrugge & Saeed Zaman, 2021. "Whose Inflation Expectations Best Predict Inflation?," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2021(19), pages 1-7, October.
    4. Kladivko, Kamil & Österholm, Pär, 2020. "Can Households Predict where the Macroeconomy is Headed?," Working Papers 2020:11, Örebro University, School of Business.
    5. Christina Anderl & Guglielmo Maria Caporale, 2023. "Forecasting inflation with a zero lower bound or negative interest rates: Evidence from point and density forecasts," Manchester School, University of Manchester, vol. 91(3), pages 171-232, June.
    6. 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.
    7. Mayukh Dass & Masoud Moradi & Fereshteh Zihagh, 2023. "Forecasting purchase rates of new products introduced in existing categories," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 385-408, September.
    8. Andrew B. Martinez, 2020. "Extracting Information from Different Expectations," Working Papers 2020-008, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    9. Pooja Kapoor & Sujata Kar, 2024. "Do Central Bank Communications Influence Survey of Professional Forecasters? An Empirical Investigation," Business Perspectives and Research, , vol. 12(1), pages 100-112, January.
    10. Lake, A., 2020. "Optimal Feasible Expectations in Economics and Finance," Cambridge Working Papers in Economics 20105, Faculty of Economics, University of Cambridge.
    11. 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.
    12. Chris Campos & Michael McMain & Mathieu Pedemonte, 2022. "Understanding Which Prices Affect Inflation Expectations," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2022(06), pages 1-7, April.
    13. Alessandro Barbarino & Travis J. Berge & Han Chen & Andrea Stella, 2020. "Which Output Gap Estimates Are Stable in Real Time and Why?," Finance and Economics Discussion Series 2020-102, Board of Governors of the Federal Reserve System (U.S.).
    14. Oinonen, Sami & Vilmi, Lauri, 2021. "Analysing euro area inflation outlook with the Phillips curve," BoF Economics Review 5/2021, Bank of Finland.
    15. Roedl, Marianne & Dupont, Genevieve, 2020. "Monetary policy implications of the COVID-19 outbreak, the social pandemic," MPRA Paper 99981, University Library of Munich, Germany.
    16. Christian Pierdzioch & Rangan Gupta & Hossein Hassani & Emmanuel Silva, 2018. "Forecasting Changes of Economic Inequality: A Boosting Approach," Working Papers 201868, University of Pretoria, Department of Economics.
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    More about this item

    Keywords

    Survey-based inflation expectations; Informational inefficiency; Boosting; Model selection; Inflation forecasting; Phillips curve;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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