Missing data is a common problem in economics studies. We propose using Mallows model averaging (MMA) to deal with this problem, which has an important advantage over its competitors in that it asymptotically achieves the lowest possible squared error. A simulation study in comparison with...

Using an empirical likelihood approach, we show that generalized linear models can still be consistently estimated even if dependent variables are not missing at random, and derive a Hausman test by comparing this estimator to the standard one.

This article examines the impact of the boundary specification problem upon the estimation of spatial autoregressive models within an instrumental variable (IV) framework. We show the usual IV estimator remains consistent and asymptotically normal, but incurs an asymptotic bias of order...