Boosting Your Instruments: Estimation with Overidentifying Inequality Moment Conditions

Abstract

This paper derives limit distributions of empirical likelihood estimators for models in which inequality moment conditions provide overidentifying information. We show that the use of this information leads to a reduction of the asymptotic mean-squared estimation error and propose asymptotically valid confidence sets for the parameters of interest. While inequality moment conditions arise in many important economic models, we use a dynamic macroeconomic model as data generating process and illustrate our methods with instrumental variable estimators of monetary policy rules. The assumption that output does not fall in response to an expansionary monetary policy shock leads to an inequality moment condition that can substantially increase the precision with which the policy rule is estimated. The results obtained in this paper extend to conventional GMM estimators.