How does heteroskedasticity affect OLS estimators?
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.
What is the relationship between heteroskedasticity and bias?
While heteroskedasticity does not cause bias in the coefficient estimates, it does make them less precise; lower precision increases the likelihood that the coefficient estimates are further from the correct population value.
How does heteroscedasticity impact model estimates?
While heteroscedasticity does not cause bias in the coefficient estimates, it does make them less precise. Lower precision increases the likelihood that the coefficient estimates are further from the correct population value. Heteroscedasticity tends to produce p-values that are smaller than they should be.
What will happen if your models get heteroscedasticity problem?
Specifically, heteroscedasticity increases the variance of the regression coefficient estimates, but the regression model doesn’t pick up on this. This makes it much more likely for a regression model to declare that a term in the model is statistically significant, when in fact it is not.
How does heteroskedasticity affect the OLS estimator?
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 to get heteroskedasticity-robust Wald statistic?
A heteroskedasticity-robust t statistic can be obtained by dividing an OSL estimator by its robust standard error (for zero null hypotheses). The usual F-statistic, however, is invalid. Instead, we need to use the heteroskedasticity-robust Wald statistic.
How to understand heteroskedasticity in econometrics Chapter 19?
Heteroskedasticity can best be understood visually. Figure 19.1.1 depicts a classic picture of a homoskedastic situation. We have drawn a regression line estimated via OLS in a simple, bivariate model. The vertical spread of the data around the predicted line appears to be fairly constant as X changes.
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.