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Quantile Regression for Spatial Data

  • Book
  • © 2013

Overview

Authors:
  1. Daniel P. McMillen

    1. Department of Economics, University of Illinois Institute of Government, Urbana, USA

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  • Emphasis on graphical interpretation of quantile regression results
  • Presents estimators designed specifically for the analysis of spatial data
  • Includes both parametric and nonparametric approaches
  • Includes both parametric and nonparametric
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Regional Science (BRIEFSREGION)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-ix
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  2. Quantile Regression: An Overview

    • Daniel P. McMillen
    Pages 1-11

  3. Linear and Nonparametric Quantile Regression

    • Daniel P. McMillen
    Pages 13-27

  4. A Quantile Regression Analysis of Assessment Regressivity

    • Daniel P. McMillen
    Pages 29-35

  5. Quantile Version of the Spatial AR Model

    • Daniel P. McMillen
    Pages 37-47

  6. Conditionally Parametric Quantile Regression

    • Daniel P. McMillen
    Pages 49-60

  7. Guide to Further Reading

    • Daniel P. McMillen
    Pages 61-63

  8. Back Matter

    Pages 65-66
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Keywords

About this book

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.

Authors and Affiliations

  • Department of Economics, University of Illinois Institute of Government, Urbana, USA

    Daniel P. McMillen

About the author

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.

Bibliographic Information

  • Book Title: Quantile Regression for Spatial Data

  • Authors: Daniel P. McMillen

  • Series Title: SpringerBriefs in Regional Science

  • DOI: https://doi.org/10.1007/978-3-642-31815-3

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Business and Economics, Economics and Finance (R0)

  • Copyright Information: The Author(s) 2013

  • Softcover ISBN: 978-3-642-31814-6Published: 01 August 2012

  • eBook ISBN: 978-3-642-31815-3Published: 01 August 2012

  • Series ISSN: 2192-0427

  • Series E-ISSN: 2192-0435

  • Edition Number: 1

  • Number of Pages: IX, 66

  • Number of Illustrations: 47 b/w illustrations

  • Topics: Regional/Spatial Science

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