What are the problems with using OLS when there is heteroscedasticity?
Heteroskedasticity has serious consequences for the OLS estimator. Although the OLS estimator remains unbiased, the estimated SE is wrong. Because of this, confidence intervals and hypotheses tests cannot be relied on. In addition, the OLS estimator is no longer BLUE.
How does heteroskedasticity affect OLS regression?
Heteroscedasticity tends to produce p-values that are smaller than they should be. This effect occurs because heteroscedasticity increases the variance of the coefficient estimates but the OLS procedure does not detect this increase.
Can a heteroscedasticity correction be a robust standard error?
However, there is a way of obtaining consistent estimates of variances and covariances of OLS estimators even if there is heteroscedasticity. White’s heteroscedasticity-corrected standard errors are known as robust standard errors.
What happens if error term μ I is heteroscedastic?
If error term ( μ i) is heteroscedastic, then the OLS estimates do not have the minimum variance property in the class of unbiased estimators, i.e. they are inefficient in small samples. Furthermore they are asymptotically inefficient. The estimated coefficients remain unbiased statistically.
Which is the best test for heteroscedasticity in OLS?
Run the Breusch-Pagan test for linear heteroscedasticity. Perform White’s IM test for heteroscedasticity. In this tutorial, we examine the residuals for heteroscedasticity. If the OLS model is well-fitted there should be no observable pattern in the residuals.
How does heteroscedasticity affect the unbiasedness property of OLS?
Also note that heteroscedasticity tends to affect cross-sectional data more than time series. Hence, unbiasedness property of OLS estimation is not affected by Heteroscedasticity. Loading… Be the first to like this.