On the Choice of Test Statistic for Conditional Moment Inequalities

Timothy Armstrong

No 1960R2, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: This paper derives asymptotic approximations to the power of Cramer-von Mises (CvM) style tests for inference on a finite dimensional parameter defined by conditional moment inequalities in the case where the parameter is set identified. Combined with power results for Kolmogorov- Smirnov (KS) tests, these results can be used to choose the optimal test statistic, weighting function and, for tests based on kernel estimates, kernel bandwidth. The results show that, in the setting considered here, KS tests are preferred to CvM tests, and that a truncated variance weighting is preferred to bounded weightings.This paper derives asymptotic approximations to the power of Cramer-von Mises (CvM) style tests for inference on a finite dimensional parameter defined by conditional moment inequalities in the case where the parameter is set identified. Combined with power results for Kolmogorov-Smirnov (KS) tests, these results can be used to choose the optimal test statistic, weighting function and, for tests based on kernel estimates, kernel bandwidth. The results show that, in the setting considered here, KS tests are preferred to CvM tests, and that a truncated variance weighting is preferred to bounded weightings.

Keywords: Moment inequalities; Relative efficiency (search for similar items in EconPapers)
JEL-codes: C10 C12 C14 (search for similar items in EconPapers)
Pages: 64 pages
Date: 2017-07
Note: Includes Supplemental Material
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Related works:
Journal Article: On the choice of test statistic for conditional moment inequalities (2018)
Working Paper: On the Choice of Test Statistic for Conditional Moment Inequalities (2016)
Working Paper: On the Choice of Test Statistic for Conditional Moment Inequalities (2014)
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