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A new approach to probabilistic surveys of professional forecasters and its application in the monetary policy context

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Abstract

In this paper we present the NBP Survey of Professional Forecasters introduced in 2011 by the National Bank of Poland. It is a new survey that allows analysis of macroeconomic forecasts of professional economists, including their probabilistic forecasts of CPI inflation, GDP growth and the NBP reference rate. In the paper we discuss in detail survey methodology, whose some elements are novel. It refers especially to the construction of probabilistic survey questions. Instead of declaring probabilities that in a certain horizon a given variable will be in pre-defined intervals, NBP SPF experts declare median and the limits of a 90-percent probability range between the 5th and 95th percentile of their subjective probability distributions. To present the benefits from the applied design of the NBP SPF, we describe the first results obtained from the NBP SPF.

Suggested Citation

  • Halina Kowalczyk & Tomasz Lyziak & Ewa Stanisławska, 2013. "A new approach to probabilistic surveys of professional forecasters and its application in the monetary policy context," NBP Working Papers 142, Narodowy Bank Polski.
    • Handle: RePEc:nbp:nbpmis:142
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    References listed on IDEAS

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    1. Rolf Scheufele, 2011. "Are Qualitative Inflation Expectations Useful to Predict Inflation?," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2011(1), pages 29-53.
    2. Yash P. Mehra, 2002. "Survey measures of expected inflation : revisiting the issues of predictive content and rationality," Economic Quarterly, Federal Reserve Bank of Richmond, issue Sum, pages 17-36.
    3. Fushang Liu & Kajal Lahiri, 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.
    4. Bharat Trehan, 2015. "Survey Measures of Expected Inflation and the Inflation Process," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(1), pages 207-222, February.
    5. D Johnson, 2002. "Triangular approximations for continuous random variables in risk analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(4), pages 457-467, April.
    6. Lyziak, Tomasz & Mackiewicz, Joanna & Stanislawska, Ewa, 2007. "Central bank transparency and credibility: The case of Poland, 1998-2004," European Journal of Political Economy, Elsevier, vol. 23(1), pages 67-87, March.
    7. Steffen Henzel & Timo Wollmershäuser, 2006. "The New Keynesian Phillips Curve and the Role of Expectations: Evidence from the Ifo World Economic Survey," CESifo Working Paper Series 1694, CESifo.
    8. Victor Zarnowitz & Louis A. Lambros, 1983. "Consensus and Uncertainty in Economic Prediction," NBER Working Papers 1171, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Timo Henckel & Gordon D. Menzies & Peter Moffat & Daniel J. Zizzo, 2019. "Three Dimensions of Central Bank Credibility and Inferential Expectations: The Euro Zone," Working Paper Series 56, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    2. Jakub Rybacki, 2019. "Forward guidance and the private forecast disagreement – case of Poland," Bank i Kredyt, Narodowy Bank Polski, vol. 50(4), pages 411-428.
    3. Svetlana Makarova, 2014. "Risk and Uncertainty: Macroeconomic Perspective," UCL SSEES Economics and Business working paper series 129, UCL School of Slavonic and East European Studies (SSEES).
    4. Henckel, Timo & Menzies, Gordon D. & Moffatt, Peter & Zizzo, Daniel J., 2019. "Three dimensions of central bank credibility and inferential expectations: The Euro zone," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 294-308.
    5. Tomasz Łyziak, 2013. "A note on central bank transparency and credibility in Poland," NBP Working Papers 162, Narodowy Bank Polski.
    6. Tomasz Łyziak, 2016. "The impact of financial crisis and low inflation environment on short-term inflation expectations in Poland," NBP Working Papers 235, Narodowy Bank Polski.

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    More about this item

    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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