Properties of Tests for Spatial Dependence in Linear Regression Models

By Luc Anselin, Serge Rey. 2010

619 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a70528440e7b057cc3abc92c36619ef643c911c3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

Citations in the corpus

The carbon dioxide emissions of firms:a spatial analysis (2013, background): “Anselin and Rey (1991) indicate that if the spatial lag LM test statistic is greater than the spatial error LM test statistic then the former model should be chosen.”
A Spatial Autocorrelation Approach for Examining the Effects of Urban Greenspace on Residential Property Values (2010, methodology): “To identify the appropriate spatial model, we followed the ad hoc decision rule ─ that between LM tests for spatial lag model and spatial error model, the one with greater test statistics and significantly different from zero is the right specification ( Anselin and Rey 1991 ).”
Out of the Projects, Still in the Hood: The Spatial Constraints on Public-Housing Residents’ Relocation in Chicago (2009, methodology): “The Local Moran statistic decomposes Moran’s I into contributions for each location, Ii .T he sum ofIi for all observations is proportional to Moran’s I ( Anselin & Rey, 1991 ).”
Empirical evidence on the effects of tax incentives (2009, neutral): “In that case, Anselin and Rey (1991) and Anselin and Florax (1995) have found, based on Monte Carlo simulations, that the larger of the two statistics indicates the correct model, which again suggests that the spatial lag model should be chosen over the spatial error model.”
Cycle commuting in Belgium: Spatial determinants and ‘re-cycling’ strategies (2011, methodology): “Lagrange multiplier (LM) diagnostics and their robust forms (Robust LM) were preferred to Moran’s I, because they help to identify the form of spatial dependence (spatial error or spatial lag) and because Moran’s I is inappropriate in the presence of heteroskedastic or non-normally distributed error”
A Moran coefficient-based mixed effects approach to investigate spatially varying relationships (2016, methodology): “The ESF approach 93 Moran ESF is based on the Moran coefficient (MC; see, Anselin and Rey, 1991), 94 which is a spatial dependence diagnostic statistic formulated as follows3: 95 Myy MCMyy C11 ′ ′ ′ = NMC .”
Spatial dependence and the representation of space in empirical models (2009, background): “Anselin and Rey (1991) showed in a Monte Carlo study that if tests for spatial lag and spatial error are both significant, the larger of the two statistics probably indicates the correct model.”
Thirty years of spatial econometrics (2010, background): “Comments from Alan Murray, Serge Rey, an anonymous referee, and the participants and organizers of these various meetings are gratefully acknowledged and contributed to the refinement of this final version.”
Happy People or Happy Places? A Multilevel Modeling Approach to the Analysis of Happiness and Well-Being (2012, background): “’’ There is a wide range of econometric studies investigating the socioeconomic and demographic determinants of happiness and well-being and there are numerous comprehensive reviews of this literature (for example, see Argyle 2001; Dolan, Peasgood, and White 2007; Frey and Stutzer 2002). Nevertheles”
Spatial Data Analysis of Regional Development in Greater Beijing, China, in a GIS Environment (2008, methodology): “The robust LM tests are c2 distributed with one degree of freedom (Anselin and Rey 1991; Anselin 1992).”