GMM inference in spatial autoregressive models

Süleyman Taşpınar*, Osman Doğan, Wim P.M. Vijverberg

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)931-954
Number of pages24
JournalEconometric Reviews
Issue number9
Publication statusPublished - 21 Oct 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2016 Taylor & Francis Group, LLC.


  • Asymptotic variance
  • efficiency
  • GMM
  • inference
  • optimal GMME
  • spatial autoregressive models
  • variance correction
  • Windmeijer correction


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