Ivan A. Canay

We have updated the paper “Specification Test in Partially Identified Models Defined by Moment Inequalities”, which is joint work with Federico Bugni and Xiaoxia Shi. This paper studies the problem of specification testing in partially identified models defined by a finite number of moment equalities and inequalities. Under the null hypothesis, there is at least one parameter value that simultaneously satisfies all of the moment (in)equalities whereas under the alternative hypothesis there is no such parameter value. This problem has not been directly addressed in the literature (except in particular cases), although several papers have suggested a test based on checking whether confidence sets for the parameters of interest are empty or not, referred to as Test BP.

We propose two new specification tests, denoted Tests RS and RC, that achieve uniform asymptotic size control and dominate Test BP in terms of power in any finite sample and in the asymptotic limit. This version of the paper in particular includes an improved version of our tests (relative to older versions), as they now require only one tuning parameter for their implementation.

We have revised the paper Inference for Functions of Partially Identified Parameters Defined by Moment Inequalities. The Cemmap working paper number is now CWP22/14 and the previous version was CWP05/14. See more information here. This paper introduces a bootstrap-based inference method for functions of the parameter vector in a moment (in)equality model. As a special case, our method yields marginal confidence sets for individual coordinates of this parameter vector. Our inference method controls asymptotic size uniformly over a large class of data distributions. The current literature describes only two other procedures that deliver uniform size control for this type of problem: projection-based and subsampling inference. Relative to projection-based procedures, our method presents three advantages: (i) it weakly dominates in terms of finite sample power, (ii) it strictly dominates in terms of asymptotic power, and (iii) it is typically less computationally demanding. Relative to subsampling, our method presents two advantages: (i) it strictly dominates in terms of asymptotic power (for reasonable choices of subsample size), and (ii) it appears to be less sensitive to the choice of its tuning parameter than subsampling is to the choice of subsample size.

Category Archives: Research Updates

Revised Specification Paper

Revised subvector paper