  Structural estimation is an important methodology in empirical economics, and
a large class of structural models are estimated through the generalized method
of moments (GMM). Traditionally, selection of structural models has been
performed based on model fit upon estimation, which take the entire observed
samples. In this paper, we propose a model selection procedure based on
cross-validation (CV), which utilizes sample-splitting technique to avoid
issues such as over-fitting. While CV is widely used in machine learning
communities, we are the first to prove its consistency in model selection in
GMM framework. Its empirical property is compared to existing methods by
simulations of IV regressions and oligopoly market model. In addition, we
propose the way to apply our method to Mathematical Programming of Equilibrium
Constraint (MPEC) approach. Finally, we perform our method to online-retail
sales data to compare dynamic market model to static model.
