IDEAS home Printed from https://ideas.repec.org/p/fip/fednsr/253.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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.
    as

    Download full text from publisher

    File URL: https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr253.html
    Download Restriction: no

    File URL: https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr253.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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, Oxford University Press, 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," Review of Economic Studies, Oxford University Press, 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.).
    5. 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.
    6. 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.
    7. Wallis, Kenneth, 2006. "A note on the calculation of entropy from histograms," MPRA Paper 52856, University Library of Munich, Germany.
    8. 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.
    9. Manzanares, Andrés & Garcí­a, Juan Angel, 2007. "Reporting biases and survey results: evidence from European professional forecasters," Working Paper Series 836, European Central Bank.
    10. Gianna Boero & Jeremy Smith & Kenneth F. 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.
    11. Peng, Amy & Yang, Ling, 2008. "Modelling uncertainty: A recursive VAR bootstrapping approach," Economics Letters, Elsevier, vol. 99(3), pages 478-481, June.
    12. Carlos Madeira & Basit Zafar, 2012. "Heterogeneus Inflation Expectations Learning and Market Outcomes," Working Papers Central Bank of Chile 667, Central Bank of Chile.
    13. 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.).
    14. 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.

    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. Kajal Lahiri & Fushang Liu, 2006. "Modelling multi‐period inflation uncertainty using a panel of density forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1199-1219, December.
    2. Lahiri, Kajal & Liu, Fushang, 2005. "ARCH models for multi-period forecast uncertainty-a reality check using a panel of density forecasts," MPRA Paper 21693, University Library of Munich, Germany.
    3. 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.
    4. Clements, Michael P., 2010. "Explanations of the inconsistencies in survey respondents' forecasts," European Economic Review, Elsevier, vol. 54(4), pages 536-549, May.
    5. Kajal Lahiri & Xuguang Sheng, 2010. "Measuring forecast uncertainty by disagreement: The missing link," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 514-538.
    6. Manzan, Sebastiano, 2021. "Are professional forecasters Bayesian?," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).
    7. Christian Grimme & Steffen Henzel & Elisabeth Wieland, 2014. "Inflation uncertainty revisited: a proposal for robust measurement," Empirical Economics, Springer, vol. 47(4), pages 1497-1523, December.
    8. 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.
    9. Michael P. Clements, 2014. "US Inflation Expectations and Heterogeneous Loss Functions, 1968–2010," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 1-14, January.
    10. Abe, Naohito & Ueno, Yuko, 2016. "The Mechanism of Inflation Expectation Formation among Consumers," RCESR Discussion Paper Series DP16-1, Research Center for Economic and Social Risks, Institute of Economic Research, Hitotsubashi University.
    11. Robert Rich & Joseph Tracy, 2021. "A Closer Look at the Behavior of Uncertainty and Disagreement: Micro Evidence from the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(1), pages 233-253, February.
    12. Michael J. Lamla & Thomas Maag, 2012. "The Role of Media for Inflation Forecast Disagreement of Households and Professional Forecasters," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(7), pages 1325-1350, October.
    13. Olivier Armantier & Wändi Bruine de Bruin & Giorgio Topa & Wilbert van der Klaauw & Basit Zafar, 2015. "Inflation Expectations And Behavior: Do Survey Respondents Act On Their Beliefs?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(2), pages 505-536, May.
    14. Fabian Krüger & Ingmar Nolte, 2011. "Disagreement, Uncertainty and the True Predictive Density," Working Paper Series of the Department of Economics, University of Konstanz 2011-43, Department of Economics, University of Konstanz.
    15. Krüger, Fabian & Nolte, Ingmar, 2016. "Disagreement versus uncertainty: Evidence from distribution forecasts," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 172-186.
    16. Gabriel Caldas Montes & Caio Ferrari Ferreira, 2019. "Does monetary policy credibility mitigate the effects of uncertainty about exchange rate on uncertainties about both inflation and interest rate?," International Economics and Economic Policy, Springer, vol. 16(4), pages 649-678, October.
    17. Clements, Michael P., 2018. "Are macroeconomic density forecasts informative?," International Journal of Forecasting, Elsevier, vol. 34(2), pages 181-198.
    18. Sheen, Jeffrey & Wang, Ben Zhe, 2021. "Measuring macroeconomic disagreement – A mixed frequency approach," Journal of Economic Behavior & Organization, Elsevier, vol. 189(C), pages 547-566.
    19. Glas, Alexander & Hartmann, Matthias, 2016. "Inflation uncertainty, disagreement and monetary policy: Evidence from the ECB Survey of Professional Forecasters," Journal of Empirical Finance, Elsevier, vol. 39(PB), pages 215-228.
    20. Fracasso, Andrea & Secchi, Angelo & Tomasi, Chiara, 2022. "Export pricing and exchange rate expectations under uncertainty," Journal of Comparative Economics, Elsevier, vol. 50(1), pages 135-152.

    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

    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:fip:fednsr:253. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/frbnyus.html .

    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: Gabriella Bucciarelli (email available below). General contact details of provider: https://edirc.repec.org/data/frbnyus.html .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.