This paper proposes a criterion for simultaneous generalized method of moments model and moment
selection: the generalized focused information criterion (GFIC). Rather than attempting to
identify the “true” specification, the GFIC chooses from a set of potentially misspecified moment
conditions and parameter restrictions to minimize the mean squared error (MSE) of a user‐specified
target parameter. The intent of the GFIC is to formalize a situation common in applied practice.
An applied researcher begins with a set of fairly weak “baseline” assumptions, assumed to be
correct, and must decide whether to impose any of a number of stronger, more controversial
“suspect” assumptions that yield parameter restrictions, additional moment conditions, or both.
Provided that the baseline assumptions identify the model, we show how to construct an
asymptotically unbiased estimator of the asymptotic MSE to select over these suspect assumptions:
the GFIC. We go on to provide results for postselection inference and model averaging that can be
applied both to the GFIC and various alternative selection criteria. To illustrate how our
criterion can be used in practice, we specialize the GFIC to the problem of selecting over
exogeneity assumptions and lag lengths in a dynamic panel model, and show that it performs well in
simulations. We conclude by applying the GFIC to a dynamic panel data model for the price
elasticity of cigarette demand.
