Journal ArticleDOI
Separable spatial modeling of spillovers and disturbances
R. Kelley Pace,Shuang Zhu +1 more
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It is shown theoretically that the dependence structure for the spillovers and disturbances can differ and that estimates from a simple separable model show little bias in all the scenarios.Abstract:
The single spatial parameter in the spatial autoregressive model affects both the estimation of spillovers and the estimation of spatial disturbances. Consequently, the spatial autoregressive model has the undesirable property that if the degree of spatial dependence in the disturbances differs from that in the spillovers, neither may be estimated correctly. We show theoretically that the dependence structure for the spillovers and disturbances can differ and conduct a Monte Carlo experiment that verifies these findings. In contrast, estimates from a simple separable model show little bias in all the scenarios. We also show differences between the spatial autoregressive model and the separable model on five empirical examples.read more
Citations
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Journal ArticleDOI
The SLX model
TL;DR: In this paper, the authors provide a comprehensive overview of the strengths and weaknesses of different spatial econometric model specifications in terms of spillover effects and advocate taking the SLX model as point of departure in case a well-founded theory indicating which model is most appropriate is lacking.
Journal ArticleDOI
The Biggest Myth in Spatial Econometrics
James P. LeSage,R. Kelley Pace +1 more
TL;DR: In this paper, the authors show that there is little theoretical basis for the commonly held belief that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models.
Journal ArticleDOI
What Regional Scientists Need to Know About Spatial Econometrics
TL;DR: In this paper, the authors ask what should regional scientists who wish to use regression relationships involving spatial data in an effort to shed light on questions of interest in regional science know about spatial econometric methods.
Journal ArticleDOI
The Biggest Myth in Spatial Econometrics
James P. LeSage,R. Kelley Pace +1 more
TL;DR: In this article, the authors show that there is little theoretical basis for the commonly held belief that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models.
Journal Article
On spatial econometric models, spillover effects, and W
TL;DR: In this article, the authors highlight the issues with an empirical application and recently proposed approaches for selecting a model specification, which are useful and promising steps forward for applied work involving spatial econometrics.
References
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Book
Introduction to spatial econometrics
TL;DR: In this article, an introduction to spatial econometric models and methods is provided that discusses spatial autoregressive processes that can be used to extend conventional regression models and an applied example that examines the relationship between commuting to work times and transportation mode choice for a sample of 3,110 US counties in the year 2000.
Journal ArticleDOI
Statistics for Spatial Data, Revised Edition.
TL;DR: This chapter discusses how to make practical use of spatial statistics in day-to-day analytical work, and some examples from the scientific literature suggest a straightforward and efficient way to do this.
Journal ArticleDOI
Applied Spatial Econometrics: Raising the Bar
TL;DR: LeSage et al. as mentioned in this paper place the key issues and implications of the new ‘introductory’ book on spatial econometrics by James LeSage & Kelley Pace (2009) in a broader perspective: the argument in favour of the spatial Durbin model, the use of indirect effects as a more valid basis for testing whether spatial spillovers are significant, use of Bayesian posterior model probabilities to determine which spatial weights matrix best describes the data.