{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

# C7D5 - HETEROSCEDASTICITY-CONSISTENT STANDARD ERRORS b OLS...

This preview shows pages 1–5. Sign up to view the full content.

HETEROSCEDASTICITY-CONSISTENT STANDARD ERRORS 1 Heteroscedasticity causes OLS standard errors to be biased is finite samples. However it can be demonstrated that they are nevertheless consistent, provided that their variances are distributed independently of the regressors. = - - = n i i i i X X X X a 1 2 ) ( ) ( = + = n i i i u a b 1 2 OLS 2 β where

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
HETEROSCEDASTICITY-CONSISTENT STANDARD ERRORS 2 Even if this is not the case, it is still possible to obtain consistent estimators. We have seen that the slope coefficient in a simple OLS regression could be decomposed as above. = + = n i i i u a b 1 2 OLS 2 β ( 29 = = = = n i u i n i i i b i a u E a 1 2 2 1 2 2 2 OLS 2 σ σ = - - = n i i i i X X X X a 1 2 ) ( ) ( where
HETEROSCEDASTICITY-CONSISTENT STANDARD ERRORS 3 We have also seen that the variance of the estimator is given by the expression above if u i is distributed independently of u j for j i . = + = n i i i u a b 1 2 OLS 2 β = - - = n i i i i X X X X a 1 2 ) ( ) ( ( 29 = = = = n i u i n i i i b i a u E a 1 2 2 1 2 2 2 OLS 2 σ σ where

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
HETEROSCEDASTICITY-CONSISTENT STANDARD ERRORS 4
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}