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Using generalized estimating equations to estimate nonlinear models with spatial data

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  • Lu, Cuicui
  • Wang, Weining
  • Wooldridge, Jeffrey M.

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.

Suggested Citation

  • Lu, Cuicui & Wang, Weining & Wooldridge, Jeffrey M., 2020. "Using generalized estimating equations to estimate nonlinear models with spatial data," IRTG 1792 Discussion Papers 2020-017, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    • Handle: RePEc:zbw:irtgdp:2020017
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    Cited by:

    1. Zimu Chen & Zhanfeng Wang & Yuan‐chin Ivan Chang, 2020. "Sequential adaptive variables and subject selection for GEE methods," Biometrics, The International Biometric Society, vol. 76(2), pages 496-507, June.
    2. Wang, Weining & Yu, Lining & Wang, Bingling, 2020. "Tail Event Driven Factor Augmented Dynamic Model," IRTG 1792 Discussion Papers 2020-022, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

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    More about this item

    Keywords

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

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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