Spatial Effects in Econometric Practice in Environmental and Resource Economics

By Luc Anselin. 2001

341 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bcd506f54e3e9bb7b77b0af85027a9cc8b93d653

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Citations in the corpus

Integrating spatial dependence into stochastic frontier analysis (2012, background): “Despite many economic phenomena being driven by spatial processes, spatial relationships have rarely been exploited in the economic literature before the late 1990s (Bockstael 1996; Anselin 2001).”
Under the hood (2002, background): “20 See Anselin and Moreno (2002) for a review of some techni-”
Errors in variables and spatial effects in hedonic house price models of ambient air quality (2008, methodology): “An additional source of error would be a discrepancy between what is predicted as air quality and what is included into the agent’s utility function as 13 See Anselin (2001a), for an extensive review of statistical issues. air quality.14 From a practical perspective, due to the nature of the kriging”
Extreme Coefficients in Geographically Weighted Regression and Their Effects on Mapping (2009, background): “Ex post interpolation of GWR coefficients may also introduce “phantom” trends or patterns across a geographic area that would not otherwise exist in the actual data used to generate those patterns (Anselin, 2001); such patterns may contribute to biased interpretation and misinformed policies (Lamber”
A Spatial Econometric Approach to the Economics of Site-Specific Nitrogen Management in Corn Production (2004, neutral): “…of y at a location is not only determined by the X at that location, but also by the X at all other locations in the system, suitably adjusted by a distance decay factor (for a more extensive treatment of the interpretation of spatial multipliers in the context of these models, see Anselin 2001d).”
Location and uptake: integrated household and GIS analysis of technology adoption and land use, with application to smallholder dairy farms in Kenya (2002, background): “At a minimum, GIS data make distance and other spatial measures more accurate or more easily computable (Anselin, 2001).”
County child poverty rates in the US: a spatial regression approach (2006, methodology): “If a causal variable cannot be identified and operationalized, then the source of the autocorrelation will remain in the error term, necessitating what is referred to as a spatial error model and estimation procedures appropriate for such a model (Anselin, 1988, 2001a; Anselin & Bera, 1998).”