A semiparametric spatial dynamic model

By Yan Sun, Hongjia Yan, Wenyang Zhang, Zudi Lu. 2014

89 citations recorded by the source corpus.

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

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/6fdd3e41d6d258aa55f4f45673558b7514923235

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In Economics and Econometrics

1. Yiguo Sun
2. Emir Malikov

Citations in the corpus

• Semiparametric Estimation and Testing of Smooth Coefficient Spatial Autoregressive Models (2017, methodology): “Given that a likelihood-based method may not be computationally feasible in many instances with moderateor large-sized samples (Kelejian & Prucha, 1999), we propose a nonparametric GMM estimator which differs from the one introduced in Sun et al. (2014).”