Non-Bayesian Testing of a Stochastic Prediction

Eddie Dekel*, Yossi Feinberg

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

We propose a method to test a prediction of the distribution of a stochastic process. In a non-Bayesian, non-parametric setting, a predicted distribution is tested using a realization of the stochastic process. A test associates a set of realizations for each predicted distribution, on which the prediction passes, so that if there are no type I errors, a prediction assigns probability 1 to its test set. Nevertheless, these test sets can be "small", in the sense that "most" distributions assign it probability 0, and hence there are "few" type II errors. It is also shown that there exists such a test that cannot be manipulated, in the sense that an uninformed predictor, who is pretending to know the true distribution, is guaranteed to fail on an uncountable number of realizations, no matter what randomized prediction he employs. The notion of a small set we use is category I, described in more detail in the paper.

Original languageEnglish (US)
Pages (from-to)893-906
Number of pages14
JournalReview of Economic Studies
Volume73
Issue number4
DOIs
StatePublished - Oct 2006

ASJC Scopus subject areas

  • Economics and Econometrics

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