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Testable Forecasts

Pomatto, Luciano (2021) Testable Forecasts. Theoretical Economics, 16 (1). pp. 129-160. ISSN 1555-7561.

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Predictions about the future are commonly evaluated through statistical tests. As shown by recent literature, many known tests are subject to adverse selection problems and cannot discriminate between forecasters who are competent and forecasters who are uninformed but predict strategically. We consider a framework where forecasters' predictions must be consistent with a paradigm, a set of candidate probability laws for the stochastic process of interest. The paper presents necessary and sufficient conditions on the paradigm under which it is possible to discriminate between informed and uninformed forecasters. We show that optimal tests take the form of likelihood-ratio tests comparing forecasters' predictions against the predictions of a hypothetical Bayesian outside observer. In addition, the paper illustrates a new connection between the problem of testing strategic forecasters and the classical Neyman-Pearson paradigm of hypothesis testing.

Item Type:Article
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URLURL TypeDescription Paper
Pomatto, Luciano0000-0002-4331-8436
Additional Information:© 2021 The Author. Licensed under the Creative Commons Attribution-NonCommercial License 4.0. Manuscript received 16 May, 2019; final version accepted 4 April, 2020; available online 28 May, 2020. I am grateful to two anonymous referees, as well as Nabil Al-Najjar, Kim Border, Andres Carvajal, Eddie Dekel, Federico Echenique, Ithzak Gilboa, Johannes Horner, Nicolas Lambert,Wojciech Olszewski,Mallesh Pai, Larry Samuelson, Alvaro Sandroni, Colin Stewart, andMax Stinchcombe for their helpful comments. I thank the Cowles Foundation for Research in Economics, where part of this research was completed, for its support and hospitality.
Funding AgencyGrant Number
Cowles Foundation for Research in EconomicsUNSPECIFIED
Subject Keywords:Strategic forecasting, hypothesis testing
Issue or Number:1
Classification Code:JEL classification: C120, D810
Record Number:CaltechAUTHORS:20190405-094352848
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94496
Deposited By: Tony Diaz
Deposited On:05 Apr 2019 16:51
Last Modified:12 Feb 2021 23:22

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