Computing Moment Inequality Models Using Constrained Optimization
22 Pages Posted: 21 Jun 2017 Last revised: 1 Aug 2017
Date Written: June 22, 2017
Inference for moment inequality models is computationally demanding, and often involves time-consuming grid search. By exploiting the equivalent formulations between unconstrained and constrained optimization, we establish new ways to compute the identified set and its confidence set in moment inequality models which overcome some of these computational hurdles. In simulations, using both linear and nonlinear moment inequality models, we show that our methods can find significantly better solutions and save considerable computing resources relative to conventional grid search. Our methods are user-friendly and can be implemented using a variety of available software packages.
Keywords: Moment Inequality Models, Constrained Optimization, MPEC, MPCC, Partial Identification, Computing Identified Set, Confidence Set
JEL Classification: C61, C63
Suggested Citation: Suggested Citation