A semiparametric spatial dynamic model

TL;DR

This paper introduces a semiparametric spatial dynamic model that extends traditional spatial autoregressive models to better analyze house prices, with estimation, model selection, and empirical validation.

Contribution

It develops a novel semiparametric model with profile likelihood estimation and model selection criteria, advancing spatial econometric analysis methods.

Findings

Model performs well in simulations

Effective in identifying parametric and nonparametric components

Provides insightful analysis of Boston house price data

Abstract

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.