We suggest a test for discovering whether a potential expert is informed of the distribution of a stochastic process. In a non-Bayesian non-parametric setting, the expert is asked to make a prediction which is tested against a single realization of the stochastic process. It is shown that by asking the expert to predict a "small" set of sequences, the test will assure that any informed expert can pass the test with probability one with respect to the actual distribution. Moreover, for the uninformed non-expert it is impossible to pass this test, in the sense that for any choice of a "small" set of sequences, only a "small" set of measures will assign a positive probability to the given set. Hence for "most" measures, the non-expert will surely fail the test. We define small as category 1 sets, described in more detail in the paper.
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Paper provided by Stanford University, Graduate School of Business in its series Research Papers with number
1856.
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
Ehud Kalai, 1995.
"Calibrated Forecasting and Merging,"
Discussion Papers
1144, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
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