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

Abstract

Abstract. In this Chapter we provide a critical review of parametric and semiparametric 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.

Does network position foster knowledge production? Evidence from international scientific collaboration network

“…When it comes to knowledge spillovers, there are two different research directions. One stream of the literature has adopted the regional knowledge production function approach based on the spatial econometrics techniques, which has been taken as a powerful tool for capturing spatial spillover effects (Basile & Mínguez, ) and is based on the assumption that geography is a channel for spillovers (Charlot et al, ; Ó hUallacháin, & Leslie, ; Ponds et al, ). The related literature has initially highlighted the spatial weight matrix, the parametric estimation methods and the importance of tangible inputs, however, recently the focus has shifted to network weight matrix, the semiparametric estimation methods, and intangible factors (Basile & Mínguez, ; Charlot et al, ; Hazır, LeSage, & Autant‐Bernard, ; Lee, ; Maggioni at al., ; Miguelez & Moreno, ; Ponds et al, ).…”

“…One stream of the literature has adopted the regional knowledge production function approach based on the spatial econometrics techniques, which has been taken as a powerful tool for capturing spatial spillover effects (Basile & Mínguez, ) and is based on the assumption that geography is a channel for spillovers (Charlot et al, ; Ó hUallacháin, & Leslie, ; Ponds et al, ). The related literature has initially highlighted the spatial weight matrix, the parametric estimation methods and the importance of tangible inputs, however, recently the focus has shifted to network weight matrix, the semiparametric estimation methods, and intangible factors (Basile & Mínguez, ; Charlot et al, ; Hazır, LeSage, & Autant‐Bernard, ; Lee, ; Maggioni at al., ; Miguelez & Moreno, ; Ponds et al, ). Another stream has taken social network analysis technique, a promising tool for capturing the structure and dynamics of relational spillovers effects, to enrich the literature on knowledge spillovers (Araújo, Gonçalves, & Taveira, ; Boschma & Ter Wal, ; Breschi & Lenzi, ; Fleming, King, & Juda, ; Gluckler, ; Ter Wal & Boschma, ).…”

The regional heterogeneity of household recycling: a spatial-econometric analysis of Swedish plastic packing waste

“…This led us not to pursue this option further. depends on coordinates, and when the functional form of the relationship between the dependent variable and the regressor is unknown (e.g., Basile and Minguez 2017).…”

Symbolic Analysis Applied to the Specification of Spatial Trends and Spatial Dependence

“…There are other spatial econometric models that are used below that share a common denominator, namely that space enters into the equation through W . However, in this paper, we explore the possibility of introducing space differently (see [ 3 , 4 ]) whenever specification tests indicate it. From this point of view, one of the contributions of the paper is to build the steps between parametric spatial models and semiparametric ones.…”