Simple Adaptive Size-Exact Testing for Full-Vector and Subvector Inference in Moment Inequality Models

Supplement to “Asymptotically Exact Inference in Conditional Moment Inequality Models”

This supplementary appendix contains auxiliary results and proofs for the main paper. Section A contains a proof of Theorem 3.1 in the case where l = dY = 1, which contains the main technical ideas of the general result, but requires less notation. Section B proposes an alternative way of obtaining critical values using the asymptotic distribution results in this paper. Section C contains proof...

Supplemental to Asymptotically Exact Inference in Conditional Moment Inequality Models

Adaptive Exact Inference in Graphical Models

Many algorithms and applications involve repeatedly solving variations of the same inference problem, for example to introduce new evidence to the model or to change conditional dependencies. As the model is updated, the goal of adaptive inference is to take advantage of previously computed quantities to perform inference more rapidly than from scratch. In this paper, we present algorithms for ...

Inference in Moment Inequality Models with Combined Data Sources

In this paper, we study the estimation and inference problems for parameters when the data set is obtained by combining data from different sources. In the case where we want to estimate the means of random variables in our data set, we consider the set of estimators that are linear combinations of certain sample averages, and find that one estimator, defined to be the Adjusted estimator, that ...

Adaptive Inference in Conditional Moment Inequality Models with a Known Contact Point

This paper designs tests for conditional moment inequality models that take advantage of prior knowledge of a value of the conditioning variable that gives the most informative bounds. Selection models provide a prototypical example, but the setting applies to other models as well. The tests use this prior knowledge to gain power while being adaptive to other aspects of the data generating proc...