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GMM Estimation of a Partially Linear Additive Spatial Error Model

Author

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  • Jianbao Chen

    (College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350117, China)

  • Suli Cheng

    (College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350117, China
    College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, China)

Abstract

This article presents a partially linear additive spatial error model (PLASEM) specification and its corresponding generalized method of moments (GMM). It also derives consistency and asymptotic normality of estimators for the case with a single nonparametric term and an arbitrary number of nonparametric additive terms under some regular conditions. In addition, the finite sample performance for our estimates is assessed by Monte Carlo simulations. Lastly, the proposed method is illustrated by analyzing Boston housing data.

Suggested Citation

  • Jianbao Chen & Suli Cheng, 2021. "GMM Estimation of a Partially Linear Additive Spatial Error Model," Mathematics, MDPI, vol. 9(6), pages 1-28, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:6:p:622-:d:517397
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    References listed on IDEAS

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    Cited by:

    1. Liu, Yu & Zhuang, Xiaoyang, 2023. "Shrinkage estimation of semi-parametric spatial autoregressive panel data model with fixed effects," Statistics & Probability Letters, Elsevier, vol. 194(C).

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