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.

Variance-Stabilizing Transformations to Correct for Heteroskedasticity

A common approach to dealing with heteroskedasticity, especially when the outcome has a skewed or otherwise unusual distribution, is to transform the outcome measure by some function ÿi = f (yi) and then to apply OLS regression to analyze the effects of the predictors on the transformed outcome:

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For example, many researchers routinely use a log transformation if their dependent variable is highly skewed, which not only reduces the skew but also tends to stabilize (make constant) the error variance. Keep in mind that this fundamentally changes the functional form of the relationship between the dependent variable and the independent variables. Other options for taking heteroskedasticity into account that maintain the original linear form of the relationship between y and ...

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