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GMM estimation of partially linear additive spatial autoregressive model

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  • Cheng, Suli
  • Chen, Jianbao

Abstract

This paper focuses on studying the estimation method of partially linear additive spatial autoregressive model (PLASARM) by combining both parametric and nonparametric terms. With the nonparametric functions approximated by local linear estimator, the generalized method of moment (GMM) estimators is proposed. The large sample properties of the estimators are derived for the case with a single nonparametric term and extended to an arbitrary number of nonparametric additive terms under some mild conditions. The small sample performance for our estimators is assessed by Monte Carlo simulation. In addition, the proposed method is used to analyze the forces of Chinese housing price.

Suggested Citation

  • Cheng, Suli & Chen, Jianbao, 2023. "GMM estimation of partially linear additive spatial autoregressive model," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    • Handle: RePEc:eee:csdana:v:182:y:2023:i:c:s0167947323000233
    DOI: 10.1016/j.csda.2023.107712
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    Cited by:

    1. Li, Bogui & Chen, Hao, 2024. "Estimation of fixed effects partially linear varying coefficient spatial autoregressive model with disturbances correlated in space and time," Finance Research Letters, Elsevier, vol. 59(C).

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