Spatial econometrics: from cross-sectional data to spatial panels

By J. Paul Elhorst. 2014

1,667 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/f83dd9719a283b716e4518799293eae02dc3891f

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In Economics and Econometrics

In Management Science and Operations Research

Citations in the corpus

Convergence in CO2 emissions: A spatial economic analysis with cross-country interactions (2018, background): “The omission of relevant spatial interaction terms in econometric analysis is of major importance as it could lead to bias/inconsistent and inefficient estimates Elhorst (2014).”
Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices (2020, neutral): “See Elhorst (2014).”
Spatial data analysis (2024, neutral): “For those keen on exploring spatial panel data, consider the works of Elhorst (2014 The Spatial Lag Model (SLX) stands out for several reasons: 1) It is straightforward in its simplicity; 2) Estimation can be performed using least squares; 3) It can be seamlessly integrated into panel data methods,…”
Regional Branching and Key Enabling Technologies: Evidence from European Patent Data (2017, methodology): “…exclusive reference to coefficients may not be reliable in ascertaining the existence of spatial spillovers (Le Sage and Pace 2009), following Elhorst (2014b), in the lower part of Table 9, we look at the effects that a change in our explanatory variables in a particular region have on the…”
The spatial pattern of demand in the early market for electric vehicles: Evidence from the United Kingdom (2018, methodology): “…a series of software packages have been used inclusive of Quantum GIS for the spatial variance analysis, GeoDa for the spatial weight construction and spatial autocorrelation analysis (Anselin et al. 2006), and the MatLab routines developed by Elhorst (2014) for the spatial regression analysis.”
Spatial Regression Models: A Systematic Comparison of Different Model Specifications Using Monte Carlo Experiments (2019, neutral): “Still, others (Elhorst, 2014; LeSage, 2014; LeSage & Pace, 2009) argue that there are strong analytical reasons to restrict the model specifications to a subset, as the SDM subsumes the SLX and SAR model, and the SDEM subsumes SLX and SEM.”
Two-Step Lasso Estimation of the Spatial Weights Matrix (2015, neutral): “4 See [23] for an overview.”