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Lecture 8: Heteroskedasticity

Lecture 8: Heteroskedasticity Causes Consequences Detection Fixes Assumption MLR5: Homoskedasticity In the multivariate case, this means that the variance of the error term does not increase or decrease with any of the explanatory variables x1 through xj. If MLR5 is untrue, we have Heteroskedasticity . 212var( | , ,.., )ju x xx Causes of Heteroskedasticity Error variance can increase as values of an independent variable increase. Ex: Regress household security expenditures on household income and other characteristics. Variance in household security expenditures will increase as income increases because you can t spend a lot on security unless you have a large income. Error variance can increase with extreme values of an independent variable (either positive or negative) Measurement error.

If heteroskedasticity is suspected to derive from a single variable, plot it against the residuals This is an ad hoc method for getting an intuitive feel for the form of heteroskedasticity in your model . Let’s see if the regression from the 2010 midterm has heteroskedasticity

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