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Estimation of a semiparametric varying-coefficient mixed regressive spatial autoregressive model

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  • Sun, Yanqing
  • Zhang, Yuanqing
  • Huang, Jianhua Z.

Abstract

A semiparametric varying-coefficient mixed regressive spatial autoregressive model is used to study covariate effects on spatially dependent responses, where the effects of some covariates are allowed to vary with other variables. A semiparametric series-based least squares estimating procedure is proposed with the introduction of instrumental variables and series approximations of the conditional expectations. The estimators for both the nonparametric and parametric components of the model are shown to be consistent and their asymptotic distributions are derived. The proposed estimators perform well in simulations. The proposed method is applied to analyze a data set on teen pregnancy to investigate effects of neighborhood as well as other social and economic factors on the teen pregnancy rate.

Suggested Citation

  • Sun, Yanqing & Zhang, Yuanqing & Huang, Jianhua Z., 2019. "Estimation of a semiparametric varying-coefficient mixed regressive spatial autoregressive model," Econometrics and Statistics, Elsevier, vol. 9(C), pages 140-155.
    • Handle: RePEc:eee:ecosta:v:9:y:2019:i:c:p:140-155
    DOI: 10.1016/j.ecosta.2017.05.005
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    4. Gao, Min & Yang, Wenzhi & Wu, Shipeng & Yu, Wei, 2022. "Asymptotic normality of residual density estimator in stationary and explosive autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).

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