Quantile Regression for Spatial Data

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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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.

• Locally Weighted Regression
• Nonparametric
• Quantile Regression
• Spatial Econometrics

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

  Daniel P. McMillen

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.

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