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Estimating fundamental cross-section dispersion from fixed event forecasts

Author

Listed:
  • Jonas Dovern

    (The Kiel Institute for the World Economy (IfW))

  • Ulrich Fritsche

    (Department for Economics and Politics, University of Hamburg, and DIW Berlin)

Abstract

A couple of recent papers have shifted the focus towards disagreement of professional forecasters. When dealing with survey data that is sampled at a frequency higher than annual and that includes only fixed event forecasts, e.g. expectation of average annual growth rates measures of disagreement across forecasters naturally are distorted by a component that mainly reflects the time varying forecast horizon. We use data from the Survey of Professional Forecasters, which reports both fixed event and fixed horizon forecasts, to evaluate different methods for extracting the ``fundamental'' component of disagreement. Based on the paper's results we suggest two methods to estimate dispersion measures from panels of fixed event forecasts: a moving average transformation of the underlying forecasts and estimation with constant forecast-horizon-effects. Both models are easy to handle and deliver equally well performing results, which show a surprisingly high correlation (up to 0.94) with the true dispersion.

Suggested Citation

  • Jonas Dovern & Ulrich Fritsche, 2008. "Estimating fundamental cross-section dispersion from fixed event forecasts," Macroeconomics and Finance Series 200801, University of Hamburg, Department of Socioeconomics.
    • Handle: RePEc:hep:macppr:200801
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    References listed on IDEAS

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    Cited by:

    1. Müller, Karsten, 2020. "German forecasters' narratives: How informative are German business cycle forecast reports?," Working Papers 23, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    2. Karsten Müller, 2022. "German forecasters’ narratives: How informative are German business cycle forecast reports?," Empirical Economics, Springer, vol. 62(5), pages 2373-2415, May.
    3. de Oliveira, Fernando Nascimento & Gaglianone, Wagner Piazza, 2020. "Expectations anchoring indexes for Brazil using Kalman filter: Exploring signals of inflation anchoring in the long term," International Economics, Elsevier, vol. 163(C), pages 72-91.
    4. Jonas Dovern & Ulrich Fritsche & Jiri Slacalek, 2012. "Disagreement Among Forecasters in G7 Countries," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1081-1096, November.
    5. James Yetman, 2018. "The perils of approximating fixed-horizon inflation forecasts with fixed-event forecasts," BIS Working Papers 700, Bank for International Settlements.
    6. Bank for International Settlements, 2014. "Globalisation, inflation and monetary policy in Asia and the Pacific," BIS Papers, Bank for International Settlements, number 77.
    7. Aaron Mehrotra & James Yetman, 2014. "How anchored are inflation expectations in Asia? Evidence from surveys of professional forecasters," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation, inflation and monetary policy in Asia and the Pacific, volume 77, pages 181-191, Bank for International Settlements.

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    More about this item

    Keywords

    survey data; dispersion; disagreement; fixed event forecasts;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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