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Alternative GMM estimators for spatial regression models

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  • Jörg Breitung
  • Christoph Wigger

Abstract

Using approximations of the score of the log-likelihood function, we derive moment conditions for estimating spatial regression models, starting with the spatial error model. Our approach results in computationally simple and robust estimators, such as a new moment estimator derived from the first-order approximation obtained by solving a quadratic moment equation, and performs similarly to existing generalized method of moments (GMM) estimators. Our estimator based on the second-order approximation resembles the GMM estimator proposed by Kelejian and Prucha in 1999. Hence, we provide an intuitive interpretation of their estimator. Additionally, we provide a convenient framework for computing the weighting matrix of the optimal GMM estimator. Heteroskedasticity robust versions of our estimators are also proposed. Furthermore, a first-order approximation for the spatial autoregressive model is considered, resulting in a computationally simple method of moment estimator. The performance of the considered estimators is compared in a Monte Carlo study.

Suggested Citation

  • Jörg Breitung & Christoph Wigger, 2018. "Alternative GMM estimators for spatial regression models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 13(2), pages 148-170, April.
    • Handle: RePEc:taf:specan:v:13:y:2018:i:2:p:148-170
    DOI: 10.1080/17421772.2018.1403644
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    Cited by:

    1. Clive Egbert Coetzee & Ewert P. J. Kleynhans & Ewert P. J. Kleynhans, 2022. "The Effect of Spatial Unemployment on the Neighbouring Regions’ Economies: A Regional Case Study of KwaZulu-Natal in South Africa," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 20(4 (Winter), pages 401-431.
    2. Li, Liyao & Yang, Zhenlin, 2020. "Estimation of fixed effects spatial dynamic panel data models with small T and unknown heteroskedasticity," Regional Science and Urban Economics, Elsevier, vol. 81(C).

    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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