Advances in Spatial Econometrics: Parametric vs. Semiparametric Spatial Autoregressive Models

被引：10

作者：

Basile, Roberto ^{[1
]}

Minguez, Roman ^{[2
]}

机构：

[1] Univ Campania Luigi Vanvitelli, Dept Econ, Corso Gran Priorato Malta,1, I-81043 Capua, CE, Italy

[2] Univ Castilla La Mancha, Cuenca, Spain

来源：

ECONOMY AS A COMPLEX SPATIAL SYSTEM: MACRO, MESO AND MICRO PERSPECTIVES | 2018年

关键词：

Spatial econometrics; Semiparametric models; DYNAMIC PANEL-DATA; GEOGRAPHICALLY WEIGHTED REGRESSION; MAXIMUM LIKELIHOOD ESTIMATORS; DEPENDENCE; HETEROGENEITY; FRAMEWORK; INFERENCE; WEAK;

D O I：

10.1007/978-3-319-65627-4_4

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

In this Chapter we provide a critical review of parametric and semi-parametric spatial econometric approaches. We focus on the capability of each class of models to fit the main features of spatial data (such as strong and weak cross-sectional dependence, spatial heterogeneity, nonlinearities, and time persistence), leaving aside the technicalities related to the estimation methods. We also provide a brief discussion of the existent software developed to estimate most of the econometric models exposed in this Chapter.

引用

页码：81 / 106

页数：26