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What point of a distribution summarises point predictions?

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  • Kröger, Sabine
  • Pierrot, Thibaud

Abstract

In this article, we study the point predictions that forecasters report when they are asked to predict the realisation of an iid random variable. We set up a laboratory experiment where the participants act as forecasters predicting the next realisation of random draws coming from different "objectively known" distributions which vary in the location of their central tendencies. As is standard in survey measures, the subjects in our experiment must report their best guess of the next draw as a forecast. We find that most of the forecasters report point predictions that are close to one of the three main central tendencies (mean, median or mode) of the distributions provided, with a majority corresponding to the mode. Our analysis also shows that when selecting a point prediction, people have in mind a numerical value (e.g. the mean or the mode) rather than a specific percentile of the underlying distribution. Only 5% of the forecasts reported during the experiment are based on a percentile while almost 60% are based on a numerical value.

Suggested Citation

  • Kröger, Sabine & Pierrot, Thibaud, 2019. "What point of a distribution summarises point predictions?," Discussion Papers, Research Unit: Market Behavior SP II 2019-212, WZB Berlin Social Science Center.
    • Handle: RePEc:zbw:wzbmbh:spii2019212
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    File URL: https://www.econstor.eu/bitstream/10419/206533/1/1680857673.pdf
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    References listed on IDEAS

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    1. Charles F. Manski, 2004. "Measuring Expectations," Econometrica, Econometric Society, vol. 72(5), pages 1329-1376, September.
    2. Manski, Charles F., 2002. "Identification of decision rules in experiments on simple games of proposal and response," European Economic Review, Elsevier, vol. 46(4-5), pages 880-891, May.
    3. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
    4. Das, J.W.M. & Dominitz, J. & van Soest, A.H.O., 1997. "Comparing Predictions and Outcomes : Theory and Application to Income Changes," Other publications TiSEM 6eef11dd-0ae4-4673-b8c0-2, Tilburg University, School of Economics and Management.
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    Cited by:

    1. Timo Dimitriadis & Andrew J. Patton & Patrick W. Schmidt, 2019. "Testing Forecast Rationality for Measures of Central Tendency," Papers 1910.12545, arXiv.org, revised Jul 2024.
    2. Paolo Crosetto & Thomas De Haan, 2022. "Comparing input interfaces to elicit belief distributions," Working Papers 2022-01, Grenoble Applied Economics Laboratory (GAEL).
    3. Alexander Dzionara & Niklas M. Witzig, 2024. "Behavioral Time Choices in Speed-Accuracy Trade-offs," Working Papers 2416, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.

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    More about this item

    Keywords

    subjective expectations; forecasting; eliciting point predictions; experiment;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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