Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230823
Authors: 
Lu, Cuicui
Wang, Weining
Wooldridge, Jeffrey M.
Year of Publication: 
2020
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2020-017
Abstract: 
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.
Subjects: 
quasi-maximum likelihood estimation
generalized estimating equations
nonlinear models
spatial dependence
count data
binary response data
FDI equation
JEL: 
C13
C21
C35
C51
Document Type: 
Working Paper

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