equal error variance
• One of the assumption of OLS regression is that error
terms have a constant variance across all value so f
• If not heteroscadisticity.
• standard errors underestimated so t ratos are larger
• More common in cross sectional data than time
• Heteroskedasticity implies that the variances (i.e.
- the dispersion around the expected mean of
zero) of the residuals are not constant, but that
they are different for different observations. This
causes a problem: if the variances are unequal,
then the relative reliability of each observation
(used in the regression analysis) is unequal. The
larger the variance, the lower should be the
importance (or weight) attached to that
• Note that the problem of heteroscedasticity is
likely to be more common in cross-sectional than
in time series data. In cross-sectional data, one
Detection of Heteroscedasticity
• Graphical methods: Looking for patterns in the plot
of the predicted dependent variable and the residual
• Formal tests: One of the best is White’s general
test for heteroscedasticity. If the graphical inspection
hints at heteroskedasticity, you must conduct a
formal test like the White’s test.
Consequences of Using OLS in the Presence of
• OLS estimation still gives unbiased coefficient estimates, but they are
no longer BLUE.
• This implies that if we still use OLS in the presence of
heteroscedasticity, our standard errors could be inappropriate and
hence any inferences we make could be misleading.
• Whether the standard errors calculated using the usual formulae are
too big or too small will depend upon the form of the
• In the presence of heteroscedasticity, the variances of OLS estimators
are not provided by the usual OLS formulas. But if we persist in
using the usual OLS formulas, the t and F tests based on them can be
highly mislead- ing, resulting in erroneous conclusions
• Use logarithm of dependent variable
• Use other method than OLS