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Heteroskedasticity in Regression: Detection and Correction
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Comment from the Stata technical group
Heteroskedasticity in Regression: Detection and Correction is an ideal reference for applied researchers who want to understand the challenges posed by heteroskedasticity and the ways to detect and address it. The book starts with a description of the consequences of heteroskedasticity. Then it reviews tests to detect heteroskedasticity and introduces different solutions to the problem. The discussions highlight the advantages and warnings of the methods presented and complement rigorous technical discussions using Stata examples with do-files available online.
Table of contents
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About the Author
Series Editor's Introduction
1. What Is Heteroskedasticity and Why Should We Care
Consequences of Heteroskedasticity
Common Forms of Heteroskedasticity
2. Detecting and Diagnosing Heteroskedasticity
Informal Diagnostics: Scatterplots and Tabulations of Mean Squared Residuals
Formal Statistical Tests: Breusch-Pagan, White, and Goldfeld-Quandt
Cautionary Note on Model Misspecification and Heteroskedasticity Testing
Application to the Other Examples
3. Variance-Stabilizing Transformations to Correct for Heteroskedasticity
Signed Modulus Power Transformation
4. Heteroskedasticity-Consistent (Robust) Standard Errors
Summary and Limitations
5. (Estimated) Generalized Least Squares Regression Model for Heteroskedasticity
Background on GLS
Estimating the EGLS Model for Heteroskedasticity
Application to Examples
Summary and Limitations
6. Choosing Among Correction Options
VST Versus HCCM or EGLS
HCCM Versus EGLS
Overall Recommendations and Extensions
Appendix: Miscellaneous Derivations and Tables
Derivation of Equation 1.5
Derivation of Equation 1.7
Derivation of Equation 2.7
Derivation of Equation 5.5
Derivation of Equation 5.9
Proof of the Equivalency of GLS to OLS Regression With Transformed Variables
Classroom and web training
Teaching with Stata
Statalist: The Stata Forum
Last updated: 16 November 2022
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