IDEAS home Printed from https://ideas.repec.org/p/bno/worpap/2021_1.html
   My bibliography  Save this paper

The Bias and Efficiency of the ECB Inflation Projections: a State Dependent Analysis

Author

Listed:
  • Eleonora Granziera
  • Pirkka Jalasjoki
  • Maritta Paloviita

Abstract

We test for bias and efficiency of the ECB inflation forecasts using a confidential dataset of ECB macroeconomic quarterly projections. We investigate whether the properties of the forecasts depend on the level of inflation, by distinguishing whether the inflation observed by the ECB at the time of forecasting is above or below the target. The forecasts are unbiased and efficient on average, however there is evidence of state dependence. In particular, the ECB tends to overpredict (underpredict) inflation at intermediate forecast horizons when inflation is below (above) target. The magnitude of the bias is larger when inflation is above the target. These results hold even after accounting for errors in the external assumptions. We also find evidence of inefficiency, in the form of underreaction to news, but only when inflation is above the target. Our findings bear important implications for the ECB forecasting process and ultimately for its communication strategy.

Suggested Citation

  • Eleonora Granziera & Pirkka Jalasjoki & Maritta Paloviita, 2021. "The Bias and Efficiency of the ECB Inflation Projections: a State Dependent Analysis," Working Paper 2021/1, Norges Bank.
  • Handle: RePEc:bno:worpap:2021_1
    as

    Download full text from publisher

    File URL: https://hdl.handle.net/11250/2755864
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Paul Hubert, 2015. "Do Central Bank Forecasts Influence Private Agents? Forecasting Performance versus Signals," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(4), pages 771-789, June.
    2. Paul Hubert, 2011. "Do central banks forecast influence private agents ? Forecasting performance vs. signals," Documents de Travail de l'OFCE 2011-20, Observatoire Francais des Conjonctures Economiques (OFCE).
    3. Paul Hubert, 2014. "FOMC Forecasts as a Focal Point for Private Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(7), pages 1381-1420, October.
    4. 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.
    5. George-Marios Angeletos & Zhen Huo & Karthik A. Sastry, 2021. "Imperfect Macroeconomic Expectations: Evidence and Theory," NBER Macroeconomics Annual, University of Chicago Press, vol. 35(1), pages 1-86.
    6. Messina, Jeffrey D. & Sinclair, Tara M. & Stekler, Herman, 2015. "What can we learn from revisions to the Greenbook forecasts?," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 54-62.
    7. Duffy, John & Heinemann, Frank, 2021. "Central bank reputation, cheap talk and transparency as substitutes for commitment: Experimental evidence," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 887-903.
    8. Sinclair, Tara M. & Joutz, Fred & Stekler, H.O., 2010. "Can the Fed predict the state of the economy?," Economics Letters, Elsevier, vol. 108(1), pages 28-32, July.
    9. 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, March.
    10. Christina D. Romer & David H. Romer, 2008. "The FOMC versus the Staff: Where Can Monetary Policymakers Add Value?," American Economic Review, American Economic Association, vol. 98(2), pages 230-235, May.
    11. Coibion, Olivier & Gorodnichenko, Yuriy & Kumar, Saten & Pedemonte, Mathieu, 2020. "Inflation expectations as a policy tool?," Journal of International Economics, Elsevier, vol. 124(C).
    12. Martin Ellison & Thomas J. Sargent, 2012. "A Defense Of The Fomc," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(4), pages 1047-1065, November.
    13. Söderström, Ulf & Iversen, Jens & LASEEN, PER & Lundvall, Henrik, 2016. "Real-Time Forecasting for Monetary Policy Analysis: The Case of Sveriges Riksbank," CEPR Discussion Papers 11203, C.E.P.R. Discussion Papers.
    14. 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.
    15. Lucia Alessi & Eric Ghysels & Luca Onorante & Richard Peach & Simon Potter, 2014. "Central Bank Macroeconomic Forecasting During the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(4), pages 483-500, October.
    16. Fawcett, Nicholas & Koerber, Lena & Masolo, Riccardo & Waldron, Matthew, 2015. "Evaluating UK point and density forecasts from an estimated DSGE model: the role of off-model information over the financial crisis," Bank of England working papers 538, Bank of England.
    17. Charemza, Wojciech & Ladley, Daniel, 2016. "Central banks’ forecasts and their bias: Evidence, effects and explanation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 804-817.
    18. Alexandre N. Kohlhas & Ansgar Walther, 2021. "Asymmetric Attention," American Economic Review, American Economic Association, vol. 111(9), pages 2879-2925, September.
    19. Hubert Paul, 2017. "Qualitative and quantitative central bank communication and inflation expectations," The B.E. Journal of Macroeconomics, De Gruyter, vol. 17(1), pages 1-41, January.
    20. El-Shagi, Makram & Giesen, Sebastian & Jung, Alexander, 2016. "Revisiting the relative forecast performances of Fed staff and private forecasters: A dynamic approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 313-323.
    21. Kontogeorgos, Georgios & Lambrias, Kyriacos, 2019. "An analysis of the Eurosystem/ECB projections," Working Paper Series 2291, European Central Bank.
    22. Loungani, Prakash & Stekler, Herman & Tamirisa, Natalia, 2013. "Information rigidity in growth forecasts: Some cross-country evidence," International Journal of Forecasting, Elsevier, vol. 29(4), pages 605-621.
    23. Hartmann, Philipp & Smets, Frank, 2018. "The first twenty years of the European Central Bank: monetary policy," Working Paper Series 2219, European Central Bank.
    24. Fred Joutz & Michael P. Clements & Herman O. Stekler, 2007. "An evaluation of the forecasts of the federal reserve: a pooled approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 121-136.
    25. Natsuki Arai, 2016. "Evaluating the Efficiency of the FOMC's New Economic Projections," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(5), pages 1019-1049, August.
    26. Sebastian Gomez-Barrero & Julian A. Parra-Polania, 2014. "Central Bank Strategic Forecasting," Contemporary Economic Policy, Western Economic Association International, vol. 32(4), pages 802-810, October.
    27. Rostagno, Massimo & Altavilla, Carlo & Carboni, Giacomo & Lemke, Wolfgang & Motto, Roberto & Saint Guilhem, Arthur & Yiangou, Jonathan, 2019. "A tale of two decades: the ECB’s monetary policy at 20," Working Paper Series 2346, European Central Bank.
    28. Maritta Paloviita & Markus Haavio & Pirkka Jalasjoki & Juha Kilponen, 2021. "What Does "Below, but Close to, 2 Percent" Mean? Assessing the ECB's Reaction Function with Real-Time Data," International Journal of Central Banking, International Journal of Central Banking, vol. 17(2), pages 125-169, June.
    29. Scotese, Carol A., 1994. "Forecast smoothing and the optimal under-utilization of information at the federal reserve," Journal of Macroeconomics, Elsevier, vol. 16(4), pages 653-670.
    30. Lahiri, Kajal & Sheng, Xuguang, 2008. "Evolution of forecast disagreement in a Bayesian learning model," Journal of Econometrics, Elsevier, vol. 144(2), pages 325-340, June.
    31. Hubert Paul, 2017. "Qualitative and quantitative central bank communication and inflation expectations," The B.E. Journal of Macroeconomics, De Gruyter, vol. 17(1), pages 1-41, January.
    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. Oinonen, Sami & Vilmi, Lauri, 2021. "Analysing euro area inflation outlook with the Phillips curve," BoF Economics Review 5/2021, Bank of Finland.
    2. Luigi Bonatti Roberto Tamborini & Roberto Tamborini, 2021. "Is High Inflation the New Challenge for Central Banks?," DEM Working Papers 2021/14, Department of Economics and Management.
    3. Darracq Pariès, Matthieu & Notarpietro, Alessandro & Kilponen, Juha & Papadopoulou, Niki & Zimic, Srečko & Aldama, Pierre & Langenus, Geert & Alvarez, Luis Julian & Lemoine, Matthieu & Angelini, Elena, 2021. "Review of macroeconomic modelling in the Eurosystem: current practices and scope for improvement," Occasional Paper Series 267, European Central Bank.
    4. Luigi Bonatti, & Andrea Fracasso & Roberto Tamborini, 2021. "What to expect from inflation expectations: theory, empirics and policy issues," DEM Working Papers 2022/1, Department of Economics and Management.

    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. Granziera, Eleonora & Jalasjoki, Pirkka & Paloviita, Maritta, 2021. "The bias and efficiency of the ECB inflation projections: a State dependent analysis," Research Discussion Papers 7/2021, Bank of Finland.
    2. repec:zbw:bofrdp:2021_007 is not listed on IDEAS
    3. Granziera, Eleonora & Jalasjoki, Pirkka & Paloviita, Maritta, 2021. "The bias and efficiency of the ECB inflation projections: A state dependent analysis," Bank of Finland Research Discussion Papers 7/2021, Bank of Finland.
    4. Messina, Jeffrey D. & Sinclair, Tara M. & Stekler, Herman, 2015. "What can we learn from revisions to the Greenbook forecasts?," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 54-62.
    5. Yoichi Tsuchiya, 2021. "The value added of the Bank of Japan's range forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 817-833, August.
    6. Ahrens, Steffen & Lustenhouwer, Joep & Tettamanzi, Michele, 2017. "The Stabilizing Role of Forward Guidance: A Macro Experiment," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168063, Verein für Socialpolitik / German Economic Association.
    7. G. Kontogeorgos & K. Lambrias, 2022. "Evaluating the Eurosystem/ECB staff macroeconomic projections: The first 20 years," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 213-229, March.
    8. Antón Sarabia Arturo & Bazdresch Santiago & Lelo-de-Larrea Alejandra, 2023. "The Influence of Central Bank's Projections and Economic Narrative on Professional Forecasters' Expectations: Evidence from Mexico," Working Papers 2023-21, Banco de México.
    9. Andrew C. Chang & Trace J. Levinson, 2023. "Raiders of the lost high‐frequency forecasts: New data and evidence on the efficiency of the Fed's forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 88-104, January.
    10. Carola Conces Binder & Rodrigo Sekkel, 2023. "Central Bank Forecasting: A Survey," Staff Working Papers 23-18, Bank of Canada.
    11. Benchimol, Jonathan & El-Shagi, Makram & Saadon, Yossi, 2022. "Do expert experience and characteristics affect inflation forecasts?," Journal of Economic Behavior & Organization, Elsevier, vol. 201(C), pages 205-226.
    12. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    13. Baumann, Ursel & Darracq Pariès, Matthieu & Westermann, Thomas & Riggi, Marianna & Bobeica, Elena & Meyler, Aidan & Böninghausen, Benjamin & Fritzer, Friedrich & Trezzi, Riccardo & Jonckheere, Jana & , 2021. "Inflation expectations and their role in Eurosystem forecasting," Occasional Paper Series 264, European Central Bank.
    14. Dovern, Jonas & Jannsen, Nils, 2017. "Systematic errors in growth expectations over the business cycle," International Journal of Forecasting, Elsevier, vol. 33(4), pages 760-769.
    15. Couture, Cody, 2021. "Financial market effects of FOMC projections," Journal of Macroeconomics, Elsevier, vol. 67(C).
    16. Paloviita, Maritta & Haavio, Markus & Jalasjoki, Pirkka & Kilponen, Juha & Vänni, Ilona, 2020. "Reading between the lines : Using text analysis to estimate the loss function of the ECB," Research Discussion Papers 12/2020, Bank of Finland.
    17. Paul Hubert, 2015. "Revisiting the Greenbook’s relative forecasting performance," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(1), pages 151-179.
    18. Peter Tillmann, 2011. "Reputation and Forecast Revisions: Evidence from the FOMC," MAGKS Papers on Economics 201128, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    19. Andrew C. Chang & Trace J. Levinson, 2020. "Raiders of the Lost High-Frequency Forecasts: New Data and Evidence on the Efficiency of the Fed's Forecasting," Finance and Economics Discussion Series 2020-090, Board of Governors of the Federal Reserve System (U.S.).
    20. Tsuchiya, Yoichi, 2023. "Assessing the World Bank’s growth forecasts," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 64-84.
    21. 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.

    More about this item

    Keywords

    forecast evaluation; forecast eciency; ination forecasts; central bank communication;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: 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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

    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:bno:worpap:2021_1. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nbgovno.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.