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In this paper, we study estimation of nonlinear models with cross sectionaldata using two-step generalized estimating equations (GEE) in the quasi-maximumlikelihood estimation (QMLE) framework. In the interest of improvingefficiency, we propose a grouping estimator to account for the potentialspatial correlation in the underlying innovations. We use a Poisson model and aNegative Binomial II model for count data and a Probit model for binaryresponse data to demonstrate the GEE procedure. Under mild weak dependencyassumptions, results on estimation consistency and asymptotic normality areprovided. Monte Carlo simulations show efficiency gain of our approach incomparison of different estimation methods for count data and binary responsedata. Finally we apply the GEE approach to study the determinants of the inflowforeign direct investment (FDI) to China.

Using generalized estimating equations to estimate nonlinear models with spatial data