Quantile Regression for Spatial Data

2013
ISBN: 978-3-642-31814-6
Verlag: Springer

Buch, Englisch, 66 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 131 g

Reihe: SpringerBriefs in Regional Science

Quantile regression analysis differs from more conventional regression models in its emphasis on distributions. Whereas standard regression procedures show how the expected value of the dependent variable responds to a change in an explanatory variable, quantile regressions imply predicted changes for the entire distribution of the dependent variable. Despite its advantages, quantile regression is still not commonly used in the analysis of spatial data. The objective of this book is to make quantile regression procedures more accessible for researchers working with spatial data sets. The emphasis is on interpretation of quantile regression results. 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. Both parametric and nonparametric versions of spatial models are considered in detail.

Zielgruppe

Research

Autoren/Hrsg.

Mcmillen, Daniel P.

Fachgebiete

• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
• Geowissenschaften Geographie | Raumplanung Regional- & Raumplanung

Inhaltsverzeichnis

1 Quantile Regression: An Overview. 2 Linear and Nonparametric Quantile Regression.- 3 A Quantile Regression Analysis of Assessment Regressivity.-4 Quantile Version of the Spatial AR Model.- 5. Conditionally Parametric Quantile Regression.- 6 Guide to Further Reading.- References.

Über Autor(innen)

Daniel McMillen is a Professor of Economics at the University of Illinois, with a joint appointment in the Institute of Government and Public Affairs. He serves as co-editor of Regional Science and Economics.