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

被引：8
|
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.
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页码：81 / 106
页数：26
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