Why does heteroskedasticity affect standard error?

Why does heteroskedasticity affect standard error?

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 white robust standard errors?

The Huber-White robust standard errors are equal to the square root of the elements on the diagional of the covariance matrix. where the elements of S are the squared residuals from the OLS method. We call these standard errors heteroskedasticity-consistent (HC) standard errors.

Do you think that in the presence of heteroscedasticity OLS estimators are biased as well as inefficient Why?

In the presence of heteroscedasticity OLS estimators are biased as well as inefficient. b. If heteroscedasticity is present, the conventional t and F tests are invalid. If a regression model is mis-specified (e.g., an important variable is omitted), the OLS residuals will show a distinct pattern.

Who is the founder of heteroscedasticity standard errors?

Other types of standard error adjustments, such as clustered standard errors, may be considered as extensions to HC standard errors. Heteroscedasticity-consistent standard errors are introduced by Friedhelm Eicker, and popularized in econometrics by Halbert White .

How are Heteroskedasticity and robust estimators related?

Standard errors based on this procedure are called (heteroskedasticity) robust standard errors or White-Huber standard errors. Or it is also known as the sandwich estimator of variance (because of how the calculation formula looks like). This procedure is reliable but entirely empirical. We do not impose any assumptions on the

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 to calculate heteroscedasticity consistent standard errors in Excel?

I want to calculate heteroscedasticity consistent standard errors in order to perform a t-test that adjusts for potential heteroscedasticity. I know that -regress- in combination with vce (robust) will produce respective standard errors. Unfortunately, my data which I want to assess is not obtained by using a regression analysis.