The Biggest Myth in Spatial Econometrics

By James Paul LeSage, R. Kelley Pace. 2014

521 citations recorded by the source corpus.

There is near universal agreement that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models. We find little theoretical basis for this commonly held belief, if estimates and inferences are based on the true partial derivatives for a well-specified spatial regression model. We conclude that this myth may have arisen from past applied work that incorrectly interpreted the model coefficients as if they were partial derivatives, or from use of misspecified models.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/ccb3f2b54f428c1aff47027397d2c568843f2824

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In Economics and Econometrics

Citations in the corpus

Spatial data analysis (2024, unknown): “There is an ongoing debate about the importance of spatial weights for spatial econometrics and about the right way of specifying weights matrices (LeSage & Pace 2014, Neumayer & Plümper 2016).”
Spatial Regression Models: A Systematic Comparison of Different Model Specifications Using Monte Carlo Experiments (2019, unknown): “However, these aspects have been discussed elsewhere (Corrado and Fingleton 2012; Elhorst and Halleck Vega 2017; LeSage and Pace 2014; Neumayer and Plu¨mper 2016), and the focus of this study remains on the model specifications, thereby assuming a correctly specified W .”
Semiparametric Estimation and Testing of Smooth Coefficient Spatial Autoregressive Models (2017, background): “…Harrison & Rubenfeld’s house price data from Gilley & Pace (1996), where we let unknown parameter func- 1Furthermore, theoretical basis for the widely-believed sensitivity of the estimates to the choice of spatial weights remains rather unclear, as recently argued by LeSage & Pace (2014).”