Quantile regression for spatial data mcmillen daniel p. Quantile Regression for Spatial Data 2019-01-30

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Quantile Regression: An Overview

quantile regression for spatial data mcmillen daniel p

We find that nursing homes are more likely to locate in the same Census tract as a hospital and are more likely to locate in tracts nearer to those containing a hospital. This unique data set allows us to estimate a location model for the region at a significantly higher level of resolution than the previous literature. The objective of this book is to make quantile regression procedures more accessible for researchers working with spatial data sets. We study recent changes in the geographic distances between small businesses and their bank lenders, using a large random sample of loans guaranteed by the Small Business Administration. © 2010 American Education Finance Association Appeal processes provide important opportunities to challenge administrative decisions, but adjudicators often lack access to information relevant to the disputed claim. We find less evidence to support the notion that thin markets are responsible for regressive distributions, whereby assessment ratios are higher for low-value homes than they are for high-value ones. Decompositions of temporal changes in the house price distribution suggest that the types of homes sold and their location do not account for the change in the price distribution.

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9783642318146

quantile regression for spatial data mcmillen daniel p

Quantile regression analysis differs from more conventional regression models in its emphasis on distributions. One type of shareable input is the close proximity of businesses and labor, that generates positive externalities which in turn lower the production cost of one business as the output of other businesses increases. The results provide further evidence that Chicago cannot be modelled adequately as other than a polycentric city. A series of examples using both simulated and actual data sets shows how readily seemingly complex quantile regression results can be interpreted with sets of well-constructed graphs. Using a rich cross section and a panel of British working-age males to identify the pairing types, we confirm the predicted career development patterns with regard to on-the-job training, promotion, and wages. We find numerous instances where manufacturing and commercial lots were present on residential blocks.

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9783642318146

quantile regression for spatial data mcmillen daniel p

Despite its advantages, quantile regression is still not commonly used in the analysis of spatial data. Annual appreciation rates in house prices are higher in neighborhoods close to the city center with large minority populations, high concentrations of poverty, and many vacant homes in 1990. We also find evidence of differences in location factors for domestic and foreign suppliers. A simple extension of the repeat sales estimator makes it possible to identify changes in the distance gradient over time. Despite unprecedented vertical growth in large cities, the economics of skyscrapers remain understudied. We surveyed tourists and foreign residents in Uganda to determine how preferences for particular protected areas are formed.

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Quantile regression for spatial data

quantile regression for spatial data mcmillen daniel p

Conditional logit models imply that much of this concentration can be explained parametrically by distance from Detroit, proximity to assembly plants, and access to the interstate highway system. Even small cities have complicated spatial patterns that are difficult to model adequately with a small number of explanatory variables. The data set includes the results of semi-annual surveys of rents in Los Angeles since 2007. Both parametric and nonparametric versions of spatial models are considered in detail. Motor vehicle and motor vehicle parts production tend to exhibit a strong degree of agglomeration. Because of this change, neither theoretical nor empirical models in urban economics now rely solely on the traditional monocentric city model of Muth 1969 and Mills 1972. Using data for the population of U.

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Quantile Regression for Spatial Data

quantile regression for spatial data mcmillen daniel p

Foreign suppliers exhibit a stronger preference to be near highways, other foreign suppliers and foreign assembly plants. The program was launched during the 2009-2010 school year and was expanded to 140 schools by 2015-16. Plant locations in the U. Though the hedonic approach is less wasteful of data than the repeat sales estimator, it relies on an accurate specification of the underlying econometric model. Stein-like procedures make efficient use of limited data when a group of variables — the structural characteristics — are expected beforehand to provide little explanatory power. © 2001 by the President and Fellows of Harvard College and the Massachusetts Institute of Technolog After a long period during which house prices were not affected by distance from Chicago's central business district, values now decline by more than 8 percent per mile.

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Quantile Regression for Spatial Data

quantile regression for spatial data mcmillen daniel p

The most common approaches for constructing house price indices — hedonic price functions and the repeat sales estimator — focus on changes over time in mean prices. New plants are more likely to be located in zip codes that are close to existing supplier plants. In an application to the location of supplier plants in the U. Building permits, as a percentage of the housing stock, are greater the larger are population growth, the proportion of units that are old built before 1940 , and the proportion of units that are new. Misspecified functional forms tend to produce biased estimates and spatially correlated errors. The number of subcenters identified for the Chicago metropolitan area rises from nine in 1970 to a forecasted 24 in 2020.

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eBook: Quantile Regression for Spatial Data von Daniel P. McMillen

quantile regression for spatial data mcmillen daniel p

This article examines whether the pattern of urban land use should have been regulated by local government in the 1920s, the decade in which many cities adopted their first zoning ordinances. 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. Whereas traditional methods simply indicate that assessment ratios decline with sale price, nonparametric approaches reveal that higher prices have a more pronounced effect on the variance than on the conditional expectation of the assessment ratio distribution, and that the most pronounced degree of regressivity occurs at very low sales prices. The procedure can be applied to either aggregated or disaggregated data, does not require detailed knowledge of the study area, and is easily reproducible by other researchers. As the number of bird species increased, tourists demonstrated increased willingness to visit a protected area, independently of all other factors. Kernel density functions summarize the effects of discrete changes in the explanatory variables.

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Quantile Regression for Spatial Data

quantile regression for spatial data mcmillen daniel p

Shopping centers, parks, lakes, and the like have local effects on variables such as housing prices, land values, and population density. Residential development was attracted to O'Hare Airport and formed some clusters of its own in between major highways. Industrial and commercial developments follow expected patterns in that, for example, they were attracted to locations nearer to O'Hare Airport and highway interchanges. Simulations of the difference in distributional differences across school finance regimes over time suggest that parametric approaches offer an incomplete description of the distributional impacts of policy changes. Failure of the homoscedasticity assumption also leads to inconsistent estimates in such common models as tobit and logit.

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