半参数变系数混合回归空间自回归模型的估计
Estimation of a Semiparametric Varying-Coefficient Mixed Regressive Spatial Autoregressive Model.
作者信息
Sun Yanqing, Zhang Yuanqing, Huang Jianhua Z
机构信息
Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
School of Finance and Business, Shanghai Normal University, Shanghai, China.
出版信息
Econom Stat. 2019 Jan;9:140-155. doi: 10.1016/j.ecosta.2017.05.005. Epub 2017 Jun 9.
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.
一个半参数变系数混合回归空间自回归模型被用于研究协变量对空间相关响应的影响，其中一些协变量的影响允许随其他变量而变化。通过引入工具变量和条件期望的级数近似，提出了一种基于半参数级数的最小二乘估计方法。结果表明，该模型非参数和参数部分的估计量是一致的，并推导了它们的渐近分布。所提出的估计量在模拟中表现良好。所提出的方法被应用于分析一个关于青少年怀孕的数据集，以研究邻里关系以及其他社会和经济因素对青少年怀孕率的影响。
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