Nonlinear Models and Small Sample Performance of the Generalized Method of Moments

In this paper I explore the issue of nonlinearity (both in the data generation process and in the functional form that establishes the relationship between the parameters and the data) regarding the poor performance of the Generalized Method of Moments (GMM) in small samples. To this purpose I build a sequence of models starting with a simple linear model and enlarging it progressively until I approximate a standard (nonlinear) neoclassical growth model. I then use simulation techniques to find the small sample distribution of the GMM estimators in each of the models