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Is OLS consistent with heteroskedasticity?
We use OLS (inefficient but) consistent estimators, and calculate an alternative (“robust”) standard error that allows for the possibility of heteroskedasticity.
How does heteroskedasticity affect standard errors?
Heteroscedasticity does not cause ordinary least squares coefficient estimates to be biased, although it can cause ordinary least squares estimates of the variance (and, thus, standard errors) of the coefficients to be biased, possibly above or below the true of population variance.
What is Heteroscedasticity consistent inference?
Heteroscedasticity-consistent standard errors are used to allow the fitting of a model that does contain heteroscedastic residuals. The first such approach was proposed by Huber (1967), and further improved procedures have been produced since for cross-sectional data, time-series data and GARCH estimation.
Can heteroskedasticity cause OLS estimators to be biased?
The only circumstance that will cause the OLS point estimates to be biased is b, omission of a relevant variable. Heteroskedasticity biases the standard errors, but not the point estimates. High (but not unitary) correlations among regressors do not cause any sort of bias.
What does a robust standard error mean?
“Robust” standard errors is a technique to obtain unbiased standard errors of OLS coefficients under heteroscedasticity. “Robust” standard errors have many labels that essentially refer all the same thing. Namely, standard errors that are computed with the sandwich estimator of variance.
What are heteroscedasticity-consistent ( HC ) standard errors?
The topic of heteroscedasticity-consistent ( HC) standard errors arises in statistics and econometrics in the context of linear regression and time series analysis. These are also known as Eicker–Huber–White standard errors (also Huber–White standard errors or White standard errors ), to recognize the contributions of Friedhelm Eicker, Peter J.
Why is heteroscedasticity a problem in OLS regression?
Heteroscedasticity is a problem because ordinary least squares (OLS) regression assumes that all residuals are drawn from a population that has a constant variance (homoscedasticity). To satisfy the regression assumptions and be able to trust the results, the residuals should have a constant variance.
When was heteroscedasticity introduced in GARCH estimation?
The first such approach was proposed by Huber (1967), and further improved procedures have been produced since for cross-sectional data, time-series data and GARCH estimation . Heteroscedasticity-consistent standard errors that differ from classical standard errors are an indicator of model misspecification.
How is heteroscedasticity used in econometrics and statistics?
The topic of heteroscedasticity-consistent ( HC) standard errors arises in statistics and econometrics in the context of linear regression and time series analysis.