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The relationship between expected inflation, disagreement, and uncertainty: evidence from matched point and density forecasts

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  • Robert W. Rich
  • Joseph Tracy

Abstract

This paper examines matched point and density forecasts of inflation from the Survey of Professional Forecasters to analyze the relationship between expected inflation, disagreement, and uncertainty. We extend previous studies through our data construction and estimation methodology. Specifically, we derive measures of disagreement and uncertainty by using a decomposition proposed in earlier research by Wallis and by applying the concept of entropy from information theory. We also undertake the empirical analysis within a seemingly unrelated regression framework. Our results offer mixed support for the propositions that disagreement is a useful proxy for uncertainty and that increases in expected inflation are accompanied by heightened inflation uncertainty. However, we document a robust, quantitatively and statistically significant positive association between disagreement and expected inflation.

Suggested Citation

  • Robert W. Rich & Joseph Tracy, 2006. "The relationship between expected inflation, disagreement, and uncertainty: evidence from matched point and density forecasts," Staff Reports 253, Federal Reserve Bank of New York.
    • Handle: RePEc:fip:fednsr:253
    Note: For a published version of this report, see Robert Rich and Joseph Tracy, "The Relationships among Expected Inflation, Disagreement, and Uncertainty: Evidence from Matched Point and Density Forecasts," Review of Economics and Statistics 92, no. 1 (February 2010): 200-7.
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    References listed on IDEAS

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    1. 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.
    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. Giordani, Paolo & Soderlind, Paul, 2003. "Inflation forecast uncertainty," European Economic Review, Elsevier, vol. 47(6), pages 1037-1059, December.
    4. Lahiri, Kajal & Teigland, Christie, 1987. "On the normality of probability distributions of inflation and GNP forecasts," International Journal of Forecasting, Elsevier, vol. 3(2), pages 269-279.
    5. Engelberg, Joseph & Manski, Charles F. & Williams, Jared, 2009. "Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 30-41.
    6. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 239-253.
    7. Kenneth F. Wallis, 2005. "Combining Density and Interval Forecasts: A Modest Proposal," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 983-994, December.
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    Citations

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    Cited by:

    1. Gianna Boero & Jeremy Smith & KennethF. Wallis, 2008. "Uncertainty and Disagreement in Economic Prediction: The Bank of England Survey of External Forecasters," Economic Journal, Royal Economic Society, vol. 118(530), pages 1107-1127, July.
    2. Wändi Bruine de Bruin & Michael F. Bryan & Simon M. Potter & Giorgio Topa & Wilbert Van der Klaauw, 2008. "Rethinking the measurement of household inflation expectations: preliminary findings," Staff Reports 359, Federal Reserve Bank of New York.
    3. Carlos Madeira & Basit Zafar, 2015. "Heterogeneous Inflation Expectations and Learning," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(5), pages 867-896, August.
    4. R?diger Bachmann & Steffen Elstner & Eric R. Sims, 2013. "Uncertainty and Economic Activity: Evidence from Business Survey Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 217-249, April.
    5. Post, Thomas & Hanewald, Katja, 2013. "Longevity risk, subjective survival expectations, and individual saving behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 86(C), pages 200-220.
    6. Esady, Vania, 2022. "Real and nominal effects of monetary shocks under time-varying disagreement," Bank of England working papers 1007, Bank of England.
    7. Manzanares, Andrés & Garcí­a, Juan Angel, 2007. "Reporting biases and survey results: evidence from European professional forecasters," Working Paper Series 836, European Central Bank.
    8. Gianna Boero & Jeremy Smith & KennethF. Wallis, 2008. "Uncertainty and Disagreement in Economic Prediction: The Bank of England Survey of External Forecasters," Economic Journal, Royal Economic Society, vol. 118(530), pages 1107-1127, July.
    9. Patricio A. Jaramillo & Juan Carlos Piantini, 2013. "Multimodality and mixture distributions: an application to a Survey of Economic Expectations," Applied Economics, Taylor & Francis Journals, vol. 45(14), pages 1801-1817, May.
    10. Thomas Post & Katja Hanewald, 2010. "Stochastic Mortality, Subjective Survival Expectations, and Individual Saving Behavior," SFB 649 Discussion Papers SFB649DP2010-040, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    11. Stefania D'Amico & Athanasios Orphanides, 2008. "Uncertainty and disagreement in economic forecasting," Finance and Economics Discussion Series 2008-56, Board of Governors of the Federal Reserve System (U.S.).
    12. Wallis, Kenneth, 2006. "A note on the calculation of entropy from histograms," MPRA Paper 52856, University Library of Munich, Germany.
    13. Peng, Amy & Yang, Ling, 2008. "Modelling uncertainty: A recursive VAR bootstrapping approach," Economics Letters, Elsevier, vol. 99(3), pages 478-481, June.
    14. Carlos Madeira & Basit Zafar, 2012. "Heterogeneus Inflation Expectations Learning and Market Outcomes," Working Papers Central Bank of Chile 667, Central Bank of Chile.
    15. Jonathan H. Wright, 2008. "Term premiums and inflation uncertainty: empirical evidence from an international panel dataset," Finance and Economics Discussion Series 2008-25, Board of Governors of the Federal Reserve System (U.S.).

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    More about this item

    Keywords

    point forecasts; density forecasts; inflation predictions; seemingly related regression; Survey of Professional Forecasters (SPF);
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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