GMM estimation and variable selection of partially linear additive spatial autoregressive model

Fang Lu, Guoliang Tian and Jing Yang ()
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Fang Lu: Hunan Normal University
Guoliang Tian: Southern University of Science and Technology
Jing Yang: Hunan Normal University

Statistical Papers, 2024, vol. 65, issue 4, No 14, 2253-2288

Abstract: Abstract The generalized method of moments (GMM) has been recognized as a particularly popular estimation procedure in terms of computational simplicity and estimation efficiency, in spatial autoregressive model. However, most existing literatures on the GMM estimation of semiparametric spatial autoregressive model were built on the local polynomial smoothing approach, which suffers from the computation burden for high dimensional models. In this paper, we propose a smooth-threshold GMM estimation and variable selection method of partially linear additive spatial autoregressive (PLASAR) model, with the nonparametric functions approximated by basis functions. The novel method is easily implemented and large sample properties are established with a unique way to prove it. This is not only the first attempting at studying PLASAR model based on the series GMM estimation, but also the first considering variable selection of the model, especially via the smooth-threshold estimating equation approach. Various Monte Carlo simulations confirm our theories and demonstrate that the proposed method performs reasonably well in finite samples. The New York leukemia incidence data is also considered for application.

Keywords: Semiparametric spatial econometric model; Spill-over effect; Generalized method of moment; Smooth-threshold estimating equation; Asymptotic properties (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00362-023-01481-6

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