Journal ArticleDOI
Spatial fixed effects and spatial dependence in a single cross-section†
TLDR
In this paper, the authors investigate the common conjecture in applied econometric work that the inclusion of spatial fixed effects in a regression specification for a single cross-sectional data set removes spatial dependence and demonstrate analytically and by means of a series of simulation experiments how evidence of the removal of spatial autocorrelation by spatial fixed effect may be spurious when the true data generating processes (DGP) takes the form of a spatial lag or spatial error dependence.Abstract:
We investigate the common conjecture in applied econometric work that the inclusion of spatial fixed effects in a regression specification for a single cross-sectional data set removes spatial dependence. We demonstrate analytically and by means of a series of simulation experiments how evidence of the removal of spatial autocorrelation by spatial fixed effects may be spurious when the true data generating processes (DGP) takes the form of a spatial lag or spatial error dependence. In addition, we also show that spatial fixed effects correctly remove spatial correlation only in the special case where the dependence is group-wise, with all observations in the same group as neighbours of each other.read more
Citations
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GIS-based spatial modeling of COVID-19 incidence rate in the continental United States.
TL;DR: A geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence across the continental United States is compiled and it suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model; these models still represent a significantly poor performance compared to the local models.
Journal ArticleDOI
Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach
Srikanta Sannigrahi,Francesco Pilla,Bidroha Basu,Arunima Sarkar Basu,Anna Mölter,Anna Mölter +5 more
TL;DR: It is found that the demographic composition, as well as key socio-economic determinants of the country, predominantly controls the high rate of mortality and casualties caused by COVID-19 in the European region.
Journal ArticleDOI
The overall mortality caused by COVID-19 in the European region is highly associated with demographic composition: A spatial regression-based approach
TL;DR: The local R2 values, which suggesting the influences of the selected demographic variables on overall casualties caused by COVID-19, was found highest in Italy and the UK, and the moderate localR2 was observed for France, Belgium, Netherlands, Ireland, Denmark, Norway, Sweden, Poland, Slovakia, and Romania.
Journal ArticleDOI
The impact of wind farm visibility on property values: A spatial difference-in-differences analysis
Yasin Sunak,Reinhard Madlener +1 more
TL;DR: In this paper, the authors investigated the potential devaluation of properties in Germany due to wind farms, using a quasi-experimental technique and applying a spatial difference-in-differences approach to various wind farm sites in the federal state of North Rhine-Westphalia.
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Combined impacts of highways and light rail transit on residential property values: a spatial hedonic price model for Phoenix, Arizona
TL;DR: In this article, the authors analyzed the positive and negative relationship between housing prices and proximity to light rail and highways in Phoenix, Arizona and found that proximity to transport nodes was associated significantly and positively with single-family detached home values.
References
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Identification of Endogenous Social Effects: The Reflection Problem
TL;DR: The authors examined the reflection problem that arises when a researcher observing the distribution of behaviour in a population tries to infer whether the average behaviour in some group influences the behaviour of the individuals that comprise the group.
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An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units
TL;DR: The authors illustrates the danger of spurious regression from this kind of misspecification, using as an example a wage regression estimated on data for individual workers that includes in the specification aggregate regressors for characteristics of geographical states.
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GMM estimation with cross sectional dependence
TL;DR: In this paper, a spatial model of dependence among agents using a metric of economic distance is presented, which provides cross-sectional data with a structure similar to that provided by the time index in time-series data.
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A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances
TL;DR: In this article, a computationally simple procedure for estimating cross-sectional spatial models that contain a spatial lag of the dependent variable as a regressor or a disturbance term that is spatially autoregressive is described.
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A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model
TL;DR: In this paper, a generalized moments estimator is proposed for the autoregressive parameter in a widely considered spatial autocorrelation model. But, as discussed in this paper, the (quasi) maximum likelihood estimator may not be computationally feasible in many cases involving moderate or large-sized samples.