Synopses & Reviews
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.
Synopsis
This monograph 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 analyze the data correctly. Within this monograph, attention is called to this commonly ignored problem and a practical guide is provided on how to proceed in terms of testing and correction. This book serves as a supplementary text for use in graduate-level instruction as well as a primer for quantitative researchers.