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Heteroskedasticity in Regression

Heteroskedasticity in Regression
Detection and Correction

June 2013 | 112 pages | SAGE Publications, Inc

This volume covers the commonly ignored topic of heteroskedasticity (unequal error variances) in regression analyses and provides a practical guide for how to proceed in terms of testing and correction. Emphasizing how to apply diagnostic tests and corrections for heteroskedasticity in actual data analyses, the book offers three approaches for dealing with heteroskedasticity:

  • variance-stabilizing transformations of the dependent variable;
  • calculating robust standard errors, or heteroskedasticity-consistent standard errors; and
  • generalized least squares estimation coefficients and standard errors.

The detection and correction of heteroskedasticity is illustrated with three examples that vary in terms of sample size and the types of units analyzed (individuals, households, U.S. states). Intended as a supplementary text for graduate-level courses and a primer for quantitative researchers, the book fills the gap between the limited coverage of heteroskedasticity provided in applied regression textbooks and the more theoretical statistical treatment in advanced econometrics textbooks.

Series Editor's Introduction
About the Authors
1. What Is Heteroskedasticity and Why Should We Care?
2. Detecting and Diagnosing Heteroskedasticity
3. Variance-Stabilizing Transformations To Correct For Heteroskedasticity
4. Heteroskedasticity Consistent (Robust) Standard Errors
5. (Estimated) Generalized Least Squares Regression Model For Heteroskedasticity
6. Choosing Among Correction Options
Appendix: Miscellaneous Derivations and Tables

Language of the book is easily understanable for undergraduate students. The way of teaching is well-organized. Limited sources on this issue makes the book extremly valuable.

Dr Mert Topcu
Department fo Economics, Nevsehir Haci Bektas Veli University
December 8, 2014

Quite important book as it clarifies the phenomenon of heteroscedasticity beyond the statements found in usual teaching books for statistics.
It contains many examples, which helps the reader to understand the concept.

However, it would have been good to implement practical advices for dealing with heteroscedasticity in SPSS or R, or in other words: with the book, the reader is able to understand the concept, but he is not fully able to deal with it in statistical programs.

Mr Martin Degen
Institute of Media and Communication, Technical University of Dresden
August 13, 2014
Key features


  • The problems of ignoring heteroskedasticity are illustrated in both statistical and practical terms, showing readers that heteroskedasticity is a problem that has real consequences for properly testing hypotheses.
  • The book includes diagnostic tools to assess the existence of the problem of heteroskedasticity and statistical techniques to analyze the data correctly.
  • Concrete examples throughout the book help readers understand the practical consequences of statistical issues and learn how to apply them to their own research.
  • Annotated Stata do-files on the Student Study Site give readers step-by-step explanations of both the purpose of sets of Stata commands and instructions on how to apply them.

Sample Materials & Chapters

Chapter 1

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