IDEAS home Printed from https://ideas.repec.org/a/taf/jnlbes/v31y2013i1p29-44.html
   My bibliography  Save this article

Real-Time Inflation Forecasting in a Changing World

Author

Listed:
  • Jan J. J. Groen
  • Richard Paap
  • Francesco Ravazzolo

Abstract

This article revisits the accuracy of inflation forecasting using activity and expectations variables. We apply Bayesian model averaging across different regression specifications selected from a set of potential predictors that includes lagged values of inflation, a host of real activity data, term structure data, (relative) price data, and surveys. In this model average, we can entertain different channels of structural instability, by either incorporating stochastic breaks in the regression parameters of each individual specification within this average, or allowing for breaks in the error variance of the overall model average, or both. Thus, our framework simultaneously addresses structural change and model uncertainty that would unavoidably affect any inflation forecast model. The different versions of our framework are used to model U.S. personal consumption expenditures (PCE) deflator and gross domestic product (GDP) deflator inflation rates for the 1960--2011 period. A real-time inflation forecast evaluation shows that averaging over many predictors in a model that at least allows for structural breaks in the error variance results in very accurate point and density forecasts, especially for the post-1984 period. Our framework is especially useful when forecasting, in real-time, the likelihood of lower-than-usual inflation rates over the medium term. This article has online supplementary materials.

Suggested Citation

  • Jan J. J. Groen & Richard Paap & Francesco Ravazzolo, 2013. "Real-Time Inflation Forecasting in a Changing World," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 29-44, January.
  • Handle: RePEc:taf:jnlbes:v:31:y:2013:i:1:p:29-44
    DOI: 10.1080/07350015.2012.727718
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07350015.2012.727718
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/07350015.2012.727718?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. James H. Stock & Mark W. Watson, 2008. "Phillips curve inflation forecasts," Conference Series ; [Proceedings], Federal Reserve Bank of Boston.
    2. Richard Clarida & Jordi Galí & Mark Gertler, 2000. "Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory," The Quarterly Journal of Economics, Oxford University Press, vol. 115(1), pages 147-180.
    3. Andrew T. Levin & Jeremy M. Piger, 2003. "Is inflation persistence intrinsic in industrial economies?," Working Papers 2002-023, Federal Reserve Bank of St. Louis.
    4. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    5. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    6. Galí, Jordi & Gertler, Mark, 1999. "Inflation Dynamics: A Structural Economic Analysis," CEPR Discussion Papers 2246, C.E.P.R. Discussion Papers.
    7. Jonathan H. Wright, 2009. "Forecasting US inflation by Bayesian model averaging," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(2), pages 131-144.
    8. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
    9. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
    10. C. K. Carter & R. Kohn, 1997. "Semiparametric Bayesian Inference for Time Series with Mixed Spectra," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 255-268.
    11. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    12. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    13. Sbordone, Argia M., 2002. "Prices and unit labor costs: a new test of price stickiness," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 265-292, March.
    14. Todd E. Clark & Michael W. McCracken, 2010. "Averaging forecasts from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 5-29, January.
    15. Groen, Jan J J & Mumtaz, Haroon, 2008. "Investigating the structural stability of the Phillips curve relationship," Bank of England working papers 350, Bank of England.
    16. Robert J. Gordon, 1997. "The Time-Varying NAIRU and Its Implications for Economic Policy," Journal of Economic Perspectives, American Economic Association, vol. 11(1), pages 11-32, Winter.
    17. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    18. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
    19. Luca Benati, 2003. "Evolving Post-World War II U.K. Economic Performance," Computing in Economics and Finance 2003 171, Society for Computational Economics.
    20. Estrella, Arturo & Hardouvelis, Gikas A, 1991. "The Term Structure as a Predictor of Real Economic Activity," Journal of Finance, American Finance Association, vol. 46(2), pages 555-576, June.
    21. Haldane, Andrew & Quah, Danny, 1999. "UK Phillips curves and monetary policy," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 259-278, October.
    22. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Forecasting Time Series Subject to Multiple Structural Breaks," Review of Economic Studies, Oxford University Press, vol. 73(4), pages 1057-1084.
    23. Diebold, Francis X. & Rudebusch, Glenn D. & Borag[caron]an Aruoba, S., 2006. "The macroeconomy and the yield curve: a dynamic latent factor approach," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 309-338.
    24. Timothy Cogley & Argia M. Sbordone, 2008. "Trend Inflation, Indexation, and Inflation Persistence in the New Keynesian Phillips Curve," American Economic Review, American Economic Association, vol. 98(5), pages 2101-2126, December.
    25. Marianne Sensier & Dick van Dijk, 2004. "Testing for Volatility Changes in U.S. Macroeconomic Time Series," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 833-839, August.
    26. Ang, Andrew & Piazzesi, Monika & Wei, Min, 2006. "What does the yield curve tell us about GDP growth?," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 359-403.
    27. Gary Koop & Simon M. Potter, 2007. "Estimation and Forecasting in Models with Multiple Breaks," Review of Economic Studies, Oxford University Press, vol. 74(3), pages 763-789.
    28. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
    29. Giordani, Paolo & Kohn, Robert & van Dijk, Dick, 2007. "A unified approach to nonlinearity, structural change, and outliers," Journal of Econometrics, Elsevier, vol. 137(1), pages 112-133, March.
    30. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    31. Gali, Jordi & Gertler, Mark, 1999. "Inflation dynamics: A structural econometric analysis," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 195-222, October.
    32. Fama, Eugene F & Bliss, Robert R, 1987. "The Information in Long-Maturity Forward Rates," American Economic Review, American Economic Association, vol. 77(4), pages 680-692, September.
    33. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 25(Win), pages 2-11.
    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. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
    2. Gbaguidi, David, 2012. "La courbe de Phillips : temps d’arbitrage et/ou arbitrage de temps," L'Actualité Economique, Société Canadienne de Science Economique, vol. 88(1), pages 87-119, mars.
    3. Gbaguidi DAVID, 2011. "Expectations Impact On The Effectiveness Of The Inflation-Real Activity Trade-Off," Theoretical and Practical Research in the Economic Fields, ASERS Publishing, vol. 2(2), pages 141-181.
    4. Gbaguidi, David Sedo, 2011. "Regime Switching in a New Keynesian Phillips Curve with Non-zero Steady-state Inflation Rate," MPRA Paper 35481, University Library of Munich, Germany.
    5. Nalan Basturk & Cem Cakmakli & Pinar Ceyhan & Herman K. van Dijk, 2013. "Posterior-Predictive Evidence on US Inflation using Phillips Curve Models with Non-Filtered Time Series," Tinbergen Institute Discussion Papers 13-011/III, Tinbergen Institute.
    6. Kevin Lansing, 2009. "Time Varying U.S. Inflation Dynamics and the New Keynesian Phillips Curve," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 12(2), pages 304-326, April.
    7. McKnight, Stephen & Mihailov, Alexander & Rumler, Fabio, 2020. "Inflation forecasting using the New Keynesian Phillips Curve with a time-varying trend," Economic Modelling, Elsevier, vol. 87(C), pages 383-393.
    8. Sophocles Mavroeidis & Mikkel Plagborg-Møller & James H. Stock, 2014. "Empirical Evidence on Inflation Expectations in the New Keynesian Phillips Curve," Journal of Economic Literature, American Economic Association, vol. 52(1), pages 124-188, March.
    9. Chengsi Zhang & Denise R. Osborn & Dong Heon Kim, 2008. "The New Keynesian Phillips Curve: From Sticky Inflation to Sticky Prices," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(4), pages 667-699, June.
    10. Carlos Medel, 2017. "Forecasting Chilean inflation with the hybrid new keynesian Phillips curve: globalisation, combination, and accuracy," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 20(3), pages 004-050, December.
    11. Richard K. Crump & Stefano Eusepi & Emanuel Moench, 2016. "The term structure of expectations and bond yields," Staff Reports 775, Federal Reserve Bank of New York.
    12. Berge, Travis J., 2018. "Understanding survey-based inflation expectations," International Journal of Forecasting, Elsevier, vol. 34(4), pages 788-801.
    13. Mazumder, Sandeep, 2011. "Cost-based Phillips Curve forecasts of inflation," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 553-567.
    14. Guido Ascari & Argia M. Sbordone, 2014. "The Macroeconomics of Trend Inflation," Journal of Economic Literature, American Economic Association, vol. 52(3), pages 679-739, September.
    15. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    16. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    17. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    18. Ferreira, Diego & Palma, Andreza Aparecida, 2015. "Forecasting Inflation with the Phillips Curve: A Dynamic Model Averaging Approach for Brazil," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 69(4), December.
    19. Carlos A. Medel, 2018. "Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach," International Economic Journal, Taylor & Francis Journals, vol. 32(3), pages 331-371, July.
    20. Diego Moccero & Shingo Watanabe & Boris Cournède, 2011. "What Drives Inflation in the Major OECD Economies?," OECD Economics Department Working Papers 854, OECD Publishing.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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

    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:taf:jnlbes:v:31:y:2013:i:1:p:29-44. 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: http://www.tandfonline.com/UBES20 .

    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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UBES20 .

    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.