Under the hood

By Luc Anselin. 2002

1,205 citations recorded by the source corpus.

This paper reviews a number of conceptual issues pertaining to the implementation of an explicit "spatial" perspective in applied econometrics. It provides an overview of the motivation for including spatial effects in regression models, both from a theory-driven as well as from a data-driven perspective. Considerable attention is paid to the inferential framework necessary to carry out estimation and testing and the different assumptions, constraints and implications embedded in the various specifications available in the literature. The review combines insights from the traditional spatial econometrics literature as well as from geostatistics, biostatistics and medical image analysis. © 2002 Elsevier Science B.V. All rights reserved.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3e4c02b303fabe43909d7ffebecbc01bc292c7bb

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Citations in the corpus

Measuring the Benefits of Neighbourhood Park Amenities: Application and Comparison of Spatial Hedonic Approaches (2010, methodology): “Specification tests To detect misspecification due to spatial autocorrelation processes and to distinguish the nature of the spatial autocorrelation in the error term (spatial error) and in the dependent variable (spatial lag), the modified Lagrange multiplier (LM) tests developed by Anselin et al. ”
Spatial autocorrelation and the selection of simultaneous autoregressive models (2007, methodology): “The spatial dependence of a location on neighbouring sites is then modelled with a variance‐covariance matrix based on the defined spatial weights matrix (for details see Cressie, 1993; Anselin, 1988, 2002; Anselin & Bera, 1998; Fortin & Dale, 2005).”
Hedonic Analysis of Impacts of Traffic Volumes on Property Values (2005, background): “Various techniques have been developed to address spatial autocorrelation in the past three decades, including pioneering work by Cliff and Ord (13) and subsequent efforts by Paelinck and Klaassen (14), Anselin (15), Dubin (16 ), Kelejian and Robinson (17 ), and others [for a comprehensive overview,”
An investigation of the spatial determinants of the local prevalence of poverty in rural Malawi (2005, background): “The choice of spatial weights matrix employed in the analysis is an important analytical decision for which there is little formal guidance (Anselin, 2002).”
Modelling hedonic residential rents for land use and transport simulation while considering spatial effects (2010, neutral): “Several approaches are available (e.g. Anselin 2002, 258; Bivand 2008, 251ff ).”
Estimators of Binary Spatial Autoregressive Models: A Monte Carlo Study (2013, neutral): “We highlight that only the latent variable can be used for the spatial lag, since both the models Y∗ = ρWY+Xβ+ and Y = ρWY+ Xβ+ are infeasible (Anselin, 2002; Beron and Vijverberg, 2004; Klier and McMillen, 2008).”
Integrating spatial dependence into stochastic frontier analysis (2012, methodology): “An alternative approach to the one shown earlier is the use of a spatial weight matrix based on distance (Anselin 2002).”
Estimation of the Spatial Weights Matrix under Structural Constraints (2013, background): “Unfortunately, the accuracy of spatial weights a¤ects profoundly the estimation of spatial dependence models (Anselin, 2002; Fingleton, 2003).”
Economic Growth and the Expansion of Urban Land in China (2010, methodology): “…with the log of the area of the urban core on the horizontal axis and the spatial lag of the log of the area of the urban core (that is, the log of the area of the urban core of each of an observation’s neighbours weighted by the spatial weight matrix) on the vertical axis (Anselin, 1995, 2002).”
Does `Smart Growth' Matter to Public Finance? (2008, methodology): “The relationship in equation (1) can be estimated with a spatial lag model (Anselin 1988, 2002), expressed as: € ei = β ∑ ωije j + X iΓ +υi .”
A Spatial Hedonic Approach to Assess the Impact of Swine Production on Residential Property Values (2009, background): “6 Conventionally, neighbors refer to locations adjacent to each other sharing common boundaries or vertexes, so, for example, a county in a regular grid will have four neighbors (rook or bishop criterion) or eight neighbors (queen criterion) (Anselin 2002).”
The spatial pattern of demand in the early market for electric vehicles: Evidence from the United Kingdom (2018, background): “One of these biases is the Ecological Fallacy Problem (Anselin, 2002), which reasons against making inferences to micro relationships from macro observations.”
Environmental Kuznets curves: A spatial econometric approach (2006, background): “Anselin [5] and Florax and van der Vlist [23] provide a recent overview of the literature.”
Spatial autocorrelation in multi-scale land use models (2003, methodology): “ Anselin (2002) describes this model as a standard regression model with spatially filtered variables.”