Using generalized estimating equations to estimate nonlinear models with spatial data

Cuicui Lu, Weining Wang and Jeffrey Wooldridge

No 2020-017, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

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.

Keywords: quasi-maximum likelihood estimation; generalized estimating equations; nonlinear models; spatial dependence; count data; binary response data; FDI equation (search for similar items in EconPapers)
JEL-codes: C13 C21 C35 C51 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-cna and nep-ore
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Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Using generalized estimating equations to estimate nonlinear models with spatial data (2018)
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