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Heteroskedasticity in Regression: Detection and Correction: Quantitative Applications in the Social Sciences, cartea 172

Autor Robert L. Kaufman
en Limba Engleză Paperback – 14 aug 2013
Heteroskedasticity in Regression: Detection and Correction, by Robert Kaufman, 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 monograph offers three approaches for dealing with heteroskedasticity: (1) variance-stabilizing transformations of the dependent variable; (2) calculating robust standard errors, or heteroskedasticity-consistent standard errors; and (3) 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.
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Specificații

ISBN-13: 9781452234953
ISBN-10: 1452234957
Pagini: 112
Dimensiuni: 140 x 216 x 7 mm
Greutate: 0.13 kg
Ediția:New.
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences

Locul publicării:Thousand Oaks, United States

Cuprins

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

Notă biografică


Descriere

This text covers the consequences of violating one of the key assumptions of Ordinary Least Squares regression (equal error variances), diagnostic tools to assess the existence of the problem of heteroskedasticity, and statistical techniques to analyse the data correctly.