GMM estimation of partially linear additive spatial autoregressive model

Suli Cheng and Jianbao Chen

Computational Statistics & Data Analysis, 2023, vol. 182, issue C

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.

Keywords: PLASARM; Local linear estimation; GMM estimation; Asymptotic normality; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:182:y:2023:i:c:s0167947323000233

DOI: 10.1016/j.csda.2023.107712

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