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Indirect Inference Estimation of Spatial Autoregressions

Author

Listed:
  • Yong Bao

    (Department of Economics, Purdue University, West Lafayette, IN 47907, USA)

  • Xiaotian Liu

    (Department of Economics, Purdue University, West Lafayette, IN 47907, USA)

  • Lihong Yang

    (School of Economics, Nanjing Audit University, Nanjing 211815, China
    National Academy of Development and Strategy, Renmin University of China, Beijing 100872, China)

Abstract

The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the properly recentered OLS estimator and proposes a new estimator that is based on the indirect inference (II) procedure. The resulting estimator can always be used regardless of the degree of aggregate influence on each spatial unit from other units and is consistent and asymptotically normal. The new estimator does not rely on distributional assumptions and is robust to unknown heteroscedasticity. Its good finite-sample performance, in comparison with existing estimators that are also robust to heteroscedasticity, is demonstrated by a Monte Carlo study.

Suggested Citation

  • Yong Bao & Xiaotian Liu & Lihong Yang, 2020. "Indirect Inference Estimation of Spatial Autoregressions," Econometrics, MDPI, vol. 8(3), pages 1-26, September.
    • Handle: RePEc:gam:jecnmx:v:8:y:2020:i:3:p:34-:d:408384
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    References listed on IDEAS

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    1. Rossi, Francesca & Robinson, Peter M., 2023. "Higher-order least squares inference for spatial autoregressions," Journal of Econometrics, Elsevier, vol. 232(1), pages 244-269.

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