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Multiscale Adaptive Inference On Conditional Moment Inequalities, Timothy B. Armstrong, Hock Peng Chan
Multiscale Adaptive Inference On Conditional Moment Inequalities, Timothy B. Armstrong, Hock Peng Chan
Cowles Foundation Discussion Papers
This paper considers inference for conditional moment inequality models using a multiscale statistic. We derive the asymptotic distribution of this test statistic and use the result to propose feasible critical values that have a simple analytic formula. We also propose critical values based on a modified bootstrap procedure and prove their asymptotic validity. The asymptotic distribution is extreme value, and the proof uses new techniques to overcome several technical obstacles. We provide power results that show that our test detects local alternatives that approach the identified set at the best possible rate under a set of conditions that hold generically …
Multiscale Adaptive Inference On Conditional Moment Inequalities, Timothy B. Armstrong, Hock Peng Chan
Multiscale Adaptive Inference On Conditional Moment Inequalities, Timothy B. Armstrong, Hock Peng Chan
Cowles Foundation Discussion Papers
This paper considers inference for conditional moment inequality models using a multiscale statistic. We derive the asymptotic distribution of this test statistic and use the result to propose feasible critical values that have a simple analytic formula, and to prove the asymptotic validity of a modified bootstrap procedure. The asymptotic distribution is extreme value, and the proof uses new techniques to overcome several technical obstacles. The test detects local alternatives that approach the identified set at the best rate in a broad class of models, and is adaptive to the smoothness properties of the data generating process. Our results also …
Multiscale Adaptive Inference On Conditional Moment Inequalities, Timothy B. Armstrong, Hock Peng Chan
Multiscale Adaptive Inference On Conditional Moment Inequalities, Timothy B. Armstrong, Hock Peng Chan
Cowles Foundation Discussion Papers
This paper considers inference for conditional moment inequality models using a multiscale statistic. We derive the asymptotic distribution of this test statistic and use the result to propose feasible critical values that have a simple analytic formula, and to prove the asymptotic validity of a modified bootstrap procedure. The asymptotic distribution is extreme value, and the proof uses new techniques to overcome several technical obstacles. The test detects local alternatives that approach the identified set at the best rate among available tests in a broad class of models, and is adaptive to the smoothness properties of the data generating process. …