We consider inference based on linear conditional moment inequalities, which arise in a wide variety of economic applications, including many structural models. We show that linear conditional structure greatly simplifies confidence set construction, allowing for computationally tractable projection inference in settings with nuisance parameters. Next, we derive least favorable critical values that avoid conservativeness due to projection. Finally, we introduce a conditional inference approach which ensures a strong form of insensitivity to slack moments, as well as a hybrid technique which combines the least favorable and conditional methods. Our conditional and hybrid approaches are new even in settings without nuisance parameters. We find good performance in simulations based on Wollmann (2018), especially for the hybrid approach.
I review a subset of the empirical tools available for competition analysis. The tools discussed are those needed for the empirical analysis of; demand, production efficiency, product repositioning, and the evolution of market structure. Where relevant I start with a brief review of tools developed in the 1990’s that have recently been incorporated into the analysis of actual policy. The focus is on providing an overview of new developments; both those that are easy to implement, and those that are not quite at that stage yet show promise.
This paper investigates progress in the development of models capable of empirically analyzing the evolution of industries. It starts with a parallel between the development of empirical frameworks for static and dynamic analysis of industries: both adapted their frameworks from models taken from economic theory. The dynamic framework has had its successes: it led to developments that have enabled us to control for dynamic phenomena in static empirical models and to useful computational theory. However when important characteristics of industries were integrated into that framework it generated complexities which both hindered empirical work on dynamics per se, and made it unrealistic as a model of agent behavior. This paper suggests a simpler alternative paradigm, one which need not maintain all the traditional theoretical restrictions, but does maintain the core theoretical idea of optimizing subject to an information set. It then discusses estimation, computation, and an example within that paradigm.
We estimate an insurer-speci c preference function which rationalizes hospital referrals for privately-insured births in California. The function is additively separable in: a hospital price paid by the insurer, the distance traveled, and plan and severity-speci c hospital xed e¤ects (capturing hospital quality). We use an inequality estimator that allows for errors in price and detailed hospital-severity interactions and obtain markedly di¤erent results than those from a logit. The estimates indicate that insurers with more capitated physicians are more responsive to price. Capitated plans send patients further to utilize similar-quality lower-priced hospitals; but the cost-quality trade-o¤ does not vary with capitation rates.