Implementing heteroskedasticity-consistent standard errors in SPSS (and SAS)

Homoskedasticity (also spelled as Homoscedasticity), or constant variance of regression error terms, is a key assumption of ordinary least squares (OLS) regression. When this assumption is violated, i.e. the errors are heteroskedastic (or heteroscedastic), the regression estimator is unbiased and consistent. However, it is less efficient and this leads to Type I error inflation or reduced statistical power for coefficient hypothesis tests.

Thus correcting for heteroskedasticity is necessary while conducting OLS. There are methods for this, which include transforming the data, use of weighted least squares (WLS) regression and generalized least squares (GLS) estimation. Another alternative is to use  heteroskedasticity-consistent standard error (HCSE) estimators of OLS parameter estimates (White, 1980).

Comparison of residuals between first order He...

Comparison of residuals between first order Heteroskedastic and Homoskedastic disturbances (Photo credit: Wikipedia)

HCSE are of four types. Standard errors from HC0 (the most common implementation) are best used for large sample sizes as these estimators are downward biased for small sample sizes. HC1, HC2, and HC3 estimators are better used for smaller samples.

Many researchers conduct their statistical analysis in STATA, which has in-built procedures for estimating standard errors using all of the HC methods. However, others use SPSS due to its pair-wise deletion capability (versus list-wise deletion in STATA) and suffer from its  lack of heteroskedasticity correction capabilities. This wonderful paper by Hayes and Cai, provides a macro (in the Appendix) that can implement HCSE estimators in SPSS. They also provide a similar macro for SAS.

Note that the macro has no error-handling procedures, hence pre-screening of the data is required. Also, missing data is handled by list-wise deletion (which might defeat the purpose of using SPSS for some users).

Another link to the paper is here.

References

Hayes, A.F. and Cai, L. (2007) , “Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation”, Behavior Research Methods, 39 – 4, 709-722, DOI: 10.3758/BF03192961.

White, H. (1980), “A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity,” Econometrica, 48, 817-838.

About these ads

One thought on “Implementing heteroskedasticity-consistent standard errors in SPSS (and SAS)

  1. That is really interesting, You are an excessively professional
    blogger. I’ve joined your feed and look ahead to seeking more of your great post. Additionally, I have shared your site in my social networks

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s