Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2020 017

Using generalized estimating equations to estimate nonlinear models with
spatial data

Cuicui Lu
Weining Wang
Jeffrey M. Wooldridge

In this paper, we study estimation of nonlinear models with cross sectional data
using two-step generalized estimating equations (GEE) in the quasi-maximum
likelihood estimation (QMLE) framework. In the interest of improving efficiency,
we propose a grouping estimator to account for the potential spatial correlation
in the underlying innovations. We use a Poisson model and a Negative Binomial II
model for count data and a Probit model for binary response data to demonstrate
the GEE procedure. Under mild weak dependency assumptions, results on estimation
consistency and asymptotic normality are provided. Monte Carlo simulations show
efficiency gain of our approach in comparison of different estimation methods
for count data and binary response data. Finally we apply the GEE approach to
study the determinants of the inflow foreign direct investment (FDI) to China.

quasi-maximum likelihood estimation; generalized estimating equations; nonlinear
models; spatial dependence; count data; binary response data; FDI equation

JEL Classification:
C13, C21, C35, C51