Selection bias in spatial econometric models

Article Abstract:

Estimators for spatial autocorrelation models with applications in urban economics and regional science are proposed. These estimators were assumed to eliminate the problems in discrete dependent variable models that cause inconsistent estimates. Three probit models with spatial autocorrelation were considered. A maximum-likelihood estimator was used for examining models on land use and values in Chicago, IL, during the 1920s. Results showed that heteroskedasticity and selection bias were present.

Econometrics, Urban economics

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A nonparametric analysis of employment density in a polycentric city

Article Abstract:

A nonparametric estimation method called locally weighted (LW) regression was created to assess employment density in the city of Chicago, IL. Unlike an OLS regression, LW regression is more precise in modeling polycentric cities, such as Chicago. LW regression helped reveal the highly centralize setting of Chicago city, with the Chicago central business district and O'Hare Airport as focal points of employment.

Research and Development in the Physical, Engineering, and Life Sciences, Economics, Research and Development in the Social Sciences and Humanities, Statistics, Analysis, Employment, Demographic aspects, Chicago, Illinois, Nonparametric statistics, Statistics (Mathematics)

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Probit with spatial autocorrelation

Article Abstract:

Two types of probit models with spatial heterogeneity was presented. One deals with models with autoregressive errors, and the other specifically takes into account heteroskedasticity. Simple probit estimators become inconsistent in the presence of heteroskedasticity, so the importance of the second model is the recognition of this limit. This makes the spatial expansion model preferable to other models.

Usage, Estimation theory

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