Spatial Econometrics

By Luc Anselin. Spatial Analysis Methods and Practice, 2007

1,158 citations recorded by the source corpus.

Spatial econometric methods deal with the incorporation of spatial interaction and spatial structure into regression analysis. The field has seen a recent and rapid growth spurred both by theoretical concerns as well as by the need to be able to apply econometric models to emerging large geocoded data bases. The review presented in this chapter outlines the basic terminology and discusses in some detail the specification of spatial effects, estimation of spatial regression models, and specification tests for spatial effects. SPATIAL ECONOMETRICS

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/f9d26eb628b66a691c15fd8b0c6dbf7e5d168786

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Citations in the corpus

The Impact of Temperature on Productivity and Labor Supply: Evidence from Indian Manufacturing (2015, methodology): “Shaded areas represent 90% confidence intervals, with standard errors corrected for serial and spatial correlation following Conley (2010).(23) A day moving from the lowest to the highest temperature bin reduces annual output by 0.”
Unified M-estimation of fixed-effects spatial dynamic models with short panels (2018, methodology): “The resulted models are referred to as spatial DPD (SDPD) models (Anselin, 2001; Anselin et al., 2008), where the spatial effects may appear in the model either in the form of spatial lag(s) of the response variable (Yu et al., 2008; Yu and Lee, 2010; Lee and Yu, 2010a; Korniotis, 2010; Elhorst,…”
The Standard Errors of Persistence (2019, neutral): “We generate noise as we did for treatments in the placebo test, using maximum likelihood estimates for detrended outcomes.”
Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics (2018, neutral): “To model this spatial autocorrelation, we used a spatial lag model (SLM) commonly applied in the spatial econometrics literature (Anselin, 2001), of the form y = ρWy+Xβ +ν , where W is a user-defined weight matrix implicitly specifying the form of spatial structure in the data, and ν is normally dis”
Democracy Does Cause Growth (2019, methodology): “Specifically, in column 8 we include a weighted average Wdyt of GDP in other countries as a covariate and instrument it using four of its lags (see Kelejian and Prucha, 1998, Anselin, 2001, and Lee, 2007 on the estimation of spatial panel models).”
Spatial data analysis (2024, neutral): “For more extensive materials on Moran’s I see for instance Kelejian & Piras (2017), Chapter 11.”
Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices (2020, positive): “Extensions to dynamic panels are provided by Anselin (2001), Baltagi et al. (2003), Kapoor et al. (2007), Baltagi et al. (2007), Yu et al. (2008) and Yu et al. (2012).”
Measuring resilience to economic shocks: an application to Spain (2018, background): “As is well known, this matrix is pre-specified, nonnegative, of order RxR and describes the arrangement of the cross-sectional units in the sample (Anselin, 1988, 2007).”
Environmental regulation, green technological innovation, and eco-efficiency: The case of Yangtze river economic belt in China (2020, background): “Further, in comparison to a general spatial economic model, SDM is also able to capture the spatial correlations between the dependent variables and the spatial spillover effects from the independent variables (Elhorst, 2014).”
On the determinants of Airbnb location and its spatial distribution (2019, background): “…former model is appropriate when the focus of interest is the assessment of the existence and the strength of spatial interaction, whereas the spatial error model is appropriate when the concern is with correcting for the potentially biasing influence of the spatial autocorrelation (Anselin, 2001).”