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A SEMIPARAMETRIC SPATIAL DYNAMIC MODEL
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DOI：10.1214/13-AOS1201.png)
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Stimulated by the Boston house price data, in this paper, we propose a semiparametric spatial dynamic model, which extends the ordinary spatial autoregressive models to accommodate the effects of some covariates associated with the house price. A profile likelihood based estimation procedure is proposed. The asymptotic normality of the proposed estimators are derived. We also investigate how to identify the parametric/nonparametric components in the proposed semiparametric model. We show how many unknown parameters an unknown bivariate function amounts to, and propose an AIC/BIC of nonparametric version for model selection. Simulation studies are conducted to examine the performance of the proposed methods. The simulation results show our methods work very well. We finally apply the proposed methods to analyze the Boston house price data, which leads to some interesting findings.
Keyword:
AIC/BIC
local linear modeling
profile likelihood
spatial interaction
期刊
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3.7
论文数:
2.8K
被引数:
2.9W
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Estimation of the covariance matrix of random effects in longitudinal studies.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances具有自回归和异方差干扰的空间自回归模型的规范和估计OAAI
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