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ISSUES IN SPATIAL DATA ANALYSIS
Author(s) -
McMillen Daniel P.
Publication year - 2010
Publication title -
journal of regional science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.171
H-Index - 79
eISSN - 1467-9787
pISSN - 0022-4146
DOI - 10.1111/j.1467-9787.2009.00656.x
Subject(s) - estimator , econometrics , lag , nonparametric statistics , flexibility (engineering) , variable (mathematics) , constant (computer programming) , measure (data warehouse) , computer science , spatial analysis , statistics , mathematics , data mining , computer network , mathematical analysis , programming language
Misspecified functional forms tend to produce biased estimates and spatially correlated errors. Imposing less structure than standard spatial lag models while being more amenable to large datasets, nonparametric and semiparametric methods offer significant advantages for spatial modeling. Fixed effect estimators have significant advantages when spatial effects are constant within well‐defined zones, but their flexibility can produce variable, inefficient estimates while failing to account adequately for smooth spatial trends. Though estimators that are designed to measure treatment effects can potentially control for unobserved variables while eliminating the need to specify a functional form, they may be biased if the variables are not constant within discrete zones.