Does heteroskedasticity affect efficiency?
Consequences of Heteroscedasticity The OLS estimators and regression predictions based on them remains unbiased and consistent. The OLS estimators are no longer the BLUE (Best Linear Unbiased Estimators) because they are no longer efficient, so the regression predictions will be inefficient too.
Does heteroskedasticity cause inefficiency?
While the ordinary least squares estimator is still unbiased in the presence of heteroscedasticity, it is inefficient and generalized least squares should be used instead.
Is the OLS estimator normal in the presence of heteroscedasticity?
More precisely, the OLS estimator in the presence of heteroscedasticity is asymptotically normal, when properly normalized and centered, with a variance-covariance matrix that differs from the case of homoscedasticity.
How does heteroscedasticity affect ordinary least squares estimates?
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.
How does heteroscedasticity affect a binary choice model?
Yet, in the context of binary choice models ( Logit or Probit ), heteroscedasticity will only result in a positive scaling effect on the asymptotic mean of the misspecified MLE (i.e. the model that ignores heteroscedasticity). As a result, the predictions which are based on the misspecified MLE will remain correct.
How to look for heteroskedasticity in a plot?
Detection of heteroskedasticity At a visual level, we can look for heteroskedasticity by examining the plot of residuals against predicted values or individual explanatory variables to see if the spread of residuals seems to depend on these variables.