Heteroskedasticity in regression : detection and correction
著者
書誌事項
Heteroskedasticity in regression : detection and correction
（Sage publications series, . Quantitative applications in the social sciences ; no. 07-172）
Sage, c2013
- : pbk
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注記
Includes bibliographical references and indexes
内容説明・目次
内容説明
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.
目次
Series Editor's Introduction
About the Authors
Acknowledgements
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
References
Appendix: Miscellaneous Derivations and Tables
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