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Strategic judgment: its game-theoretic foundations,its econometric elicitation

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  • Emilio Zanetti Chini

Abstract

We provide a new frequentist methodology that detects forecasting bias due to strategic interaction. This is based on a new environment, named Scoring Structure, where a Forecast User interacts with a Forecast Producer and Reality. A formal test for the null hypothesis of linearity in Scoring Structure is introduced. Linearity implies that forecasts are strategically coherent with evaluations and vice-versa. The new test has good small-sample properties and behaves consistently with theoretical requirements. We illustrate the use of the Scoring Structure and the coherence test via two case studies on the assessment of the probability of recessions for the U.S. economy and the evaluation of Norges Bank's Fan Charts of Output Gap. These support the endemic nature of the strategic judgment in Macroeconomics. Finally, we discuss the economic interpretation of the results obtained by our approach.

Suggested Citation

  • Emilio Zanetti Chini, 2019. "Strategic judgment: its game-theoretic foundations,its econometric elicitation," Working Papers in Public Economics 190, University of Rome La Sapienza, Department of Economics and Law.
    • Handle: RePEc:sap:wpaper:wp190
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    References listed on IDEAS

    as
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    3. Vladimir Vovk & Glenn Shafer, 2005. "Good randomized sequential probability forecasting is always possible," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 747-763, November.
    4. Terasvirta, Timo & Tjostheim, Dag & Granger, Clive W. J., 2010. "Modelling Nonlinear Economic Time Series," OUP Catalogue, Oxford University Press, number 9780199587155, Decembrie.
    5. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Journal of Econometrics, Elsevier, vol. 163(2), pages 215-230, August.
    6. Tilmann Gneiting & Roopesh Ranjan, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 411-422, July.
    7. Engel, J. & Haugh, D. & Pagan, A., 2005. "Some methods for assessing the need for non-linear models in business cycle analysis," International Journal of Forecasting, Elsevier, vol. 21(4), pages 651-662.
    8. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    9. Gneiting, Tilmann & Ranjan, Roopesh, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 411-422.
    10. repec:hal:journl:peer-00834423 is not listed on IDEAS
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    More about this item

    Keywords

    Business Cycle; Predictive Density; Forecast Evaluation; Coherence Testing; Scoring Rules and Structures;
    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
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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