Prequential testing of a forecaster is known to be manipulable if the test must pass an informed forecaster for all possible true distributions. Stewart (2011) provides a non-manipulable prequential likelihood test that only fails an informed forecaster on a small, category I, set of distributions. We present a prequential test based on calibration that also fails the informed forecaster on at most a category I set of true distributions and is non-manipulable. Our construction sheds light on the relationship between likelihood and calibration with respect to the distributions they reject.
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- Colin Stewart, 2009.
"Nonmanipulable Bayesian Testing,"
tecipa-360, University of Toronto, Department of Economics.
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1144R, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
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1957, Stanford University, Graduate School of Business.
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1425, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
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Econometric Society, vol. 76(6), pages 1437-1466, November.
- Lehrer, Ehud, 2001. "Any Inspection Is Manipulable," Econometrica, Econometric Society, vol. 69(5), pages 1333-47, September.
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