{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

EMET2007 Lecture 7

# Variability of the unobserved influences does not

This preview shows page 1. Sign up to view the full content.

This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: lity of the unobserved influences does not dependent on the value of the explanatory variable Lecture 7 (heteroscedasticity) EMET2007/6007 24 th April 2013 7 / 34 An example for heteroskedasticity: Wage and education The variance of the unobserved determinants of wages increases with the level of education Lecture 7 (heteroscedasticity) EMET2007/6007 24 th April 2013 8 / 34 Consequences of heteroscedasticity for OLS Some things do not change: OLS still unbiased and consistent under heteroscedasticty! Also, interpretation of R-squared is not changed R2 t 1 σ2 ε σ2 y σ2 Unconditional error variance is una¤ected by heteroscedasticity ε (which refers to the conditional error variance) However, some things do change: Heteroscedasticity invalidates variance formulas for OLS estimators The usual F-tests and t-tests are not valid under heteroscedasticity Under heteroscedasticity, OLS is no longer the best linear unbiased estimator (BLUE); there may be more e¢ cient linear estimators Lecture 7 (heteroscedasticity) EMET2007/6007 24 th April 2013 9 / 34 How can I tell if I have it? Anecdotal evidence There are a number of graphical ways of observing whether we have heteroscedasticity We could plot the bi or b2 against yi or against yi or against any of ε εi b the xji If εt s N 0, σ2 then E (jεi j) = σ. So the absolute value of the ε residuals jbi j should resemble the standard deviation at each point i We could plot the jbi j against yi or against any of the xji ε Note that the relationship may not be linea...
View Full Document

{[ snackBarMessage ]}

Ask a homework question - tutors are online