Issues in Spatial Data Analysis

By Daniel P. McMillen. 2010

221 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bbb8b67c1e2e3ec57187a5c076315386d23d210b

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Citations in the corpus

On spatial econometric models, spillover effects, and W (2013, methodology): “McMillen's (2012) critique mainly focuses on the limitations of the spatial lag model (SAR) and the spatial error model (SEM), and is partly based on previous work (McMillen, 2003, 2010).”
Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria (2014, methodology): “By preventing these limitations, nonparametric locally weighted regression procedures (LWR; Cleveland and Devlin, 1988) offer significant advantages (McMillen and Redfearn, 2010; McMillen, 2010).”
Evaluating multiple spatial dimensions of economic growth in Brazil using spatial panel data models (2016, background): “Except from Resende (2011) and Resende (2013)3 the studies thus far have only investigated the determinants of economic growth at a single spatial scale to infer the consistency of spatial growth models with reality (e.”
The Slx Model (2015, methodology): “For this reason, they strongly argue for the use of more substantive theory in empirical spatial econometric modeling, especially regarding W.4 While McMillen (2010, 2012) advocates the use of nonparametric and semiparametric methods as an alternative to simply imposing common specifications of W wh”
The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models (2014, background): “But, as emphasized by McMillen (2010), the use of a parametric spatial error structure is “troublesome because it requires the researcher to specify the actual structure of the errors”, while it is much easier to justify a parametric spatial lag structure when accommodating spatial dependence.”
Mostly pointless spatial econometrics (2012, methodology): “Others have made similar arguments although perhaps not as forcefully (see, for example, McMillen, 2010a, 2010b).”
A Spatial Difference-in-Differences Estimator to Evaluate the Effect of Change in Public Mass Transit Systems on House Prices (2014, background): “The DID estimator has the advantage of eliminating the problem related to the omission of important variables that are constant over time, and some possible problems related to the functional form (McMillen, 2010).”