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Estimation and testing for a partially linear single-index spatial regression model

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  • Yan Sun
  • Yueqin Wu

Abstract

Observations recorded on ‘locations’ usually exhibit spatial dependence. In an effort to take into account both the spatial dependence and the possible underlying non-linear relationship, a partially linear single-index spatial regression model is proposed. This paper establishes the estimators of the unknowns. Moreover, it builds a generalized F-test to determine whether or not the data provide evidence on using linear settings in empirical studies. Their asymptotic properties are derived. Monte Carlo simulations indicate that the estimators and test statistic perform well. The analysis of Chinese house price data shows the existence of both spatial dependence and a non-linear relationship.

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  • Yan Sun & Yueqin Wu, 2018. "Estimation and testing for a partially linear single-index spatial regression model," Spatial Economic Analysis, Taylor & Francis Journals, vol. 13(4), pages 473-489, October.
    • Handle: RePEc:taf:specan:v:13:y:2018:i:4:p:473-489
    DOI: 10.1080/17421772.2018.1506150
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    Cited by:

    1. Zhiyong Chen & Jianbao Chen, 2022. "Bayesian analysis of partially linear, single-index, spatial autoregressive models," Computational Statistics, Springer, vol. 37(1), pages 327-353, March.
    2. Fang Lu & Jing Yang & Xuewen Lu, 2022. "One-step oracle procedure for semi-parametric spatial autoregressive model and its empirical application to Boston housing price data," Empirical Economics, Springer, vol. 62(6), pages 2645-2671, June.
    3. Cheng, Suli & Chen, Jianbao, 2023. "GMM estimation of partially linear additive spatial autoregressive model," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).

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