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GMM inference in spatial autoregressive models

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  • Süleyman Taşpınar
  • Osman Doğan
  • Wim P. M. Vijverberg

Abstract

In this study, we investigate the finite sample properties of the optimal generalized method of moments estimator (OGMME) for a spatial econometric model with a first-order spatial autoregressive process in the dependent variable and the disturbance term (for short SARAR(1, 1)). We show that the estimated asymptotic standard errors for spatial autoregressive parameters can be substantially smaller than their empirical counterparts. Hence, we extend the finite sample variance correction methodology of Windmeijer (2005) to the OGMME for the SARAR(1, 1) model. Results from simulation studies indicate that the correction method improves the variance estimates in small samples and leads to more accurate inference for the spatial autoregressive parameters. For the same model, we compare the finite sample properties of various test statistics for linear restrictions on autoregressive parameters. These tests include the standard asymptotic Wald test based on various GMMEs, a bootstrapped version of the Wald test, two versions of the C(α) test, the standard Lagrange multiplier (LM) test, the minimum chi-square test (MC), and two versions of the generalized method of moments (GMM) criterion test. Finally, we study the finite sample properties of effects estimators that show how changes in explanatory variables impact the dependent variable.

Suggested Citation

  • Süleyman Taşpınar & Osman Doğan & Wim P. M. Vijverberg, 2018. "GMM inference in spatial autoregressive models," Econometric Reviews, Taylor & Francis Journals, vol. 37(9), pages 931-954, October.
    • Handle: RePEc:taf:emetrv:v:37:y:2018:i:9:p:931-954
    DOI: 10.1080/00927872.2016.1178885
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    Cited by:

    1. Ye Yang & Osman Doğan & Süleyman Taşpınar, 2021. "Fast estimation of matrix exponential spatial models," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-50, December.
    2. Zhang, Wenjie & Quan, Hao & Srinivasan, Dipti, 2018. "Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination," Energy, Elsevier, vol. 160(C), pages 810-819.
    3. Konstantinidi, Antri & Kourtellos, Andros & Sun, Yiguo, 2023. "Social threshold regression," Journal of Econometrics, Elsevier, vol. 235(2), pages 2057-2081.
    4. Deng, Mingyu & Wang, Mingxi, 2022. "Artificial regression test diagnostics for impact measures in spatial models," Economics Letters, Elsevier, vol. 217(C).

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