What does it mean when a regression is Heteroscedastic?
Heteroskedasticity refers to situations where the variance of the residuals is unequal over a range of measured values. When running a regression analysis, heteroskedasticity results in an unequal scatter of the residuals (also known as the error term).
How do you test for heteroskedasticity in regression?
To check for heteroscedasticity, you need to assess the residuals by fitted value plots specifically. Typically, the telltale pattern for heteroscedasticity is that as the fitted values increases, the variance of the residuals also increases.
What is are the consequences of a regression model is Heteroscedastic?
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.
Is heteroskedasticity good or bad?
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 do you do if your data is Heteroscedastic?
If your data is heteroscedastic, it would be inadvisable to run regression on the data as is. There are a couple of things you can try if you need to run regression: Give data that produces a large scatter less weight. Transform the Y variable to achieve homoscedasticity.
How do you fix heteroskedasticity in regression?
Another way to fix heteroscedasticity is to use weighted regression. This type of regression assigns a weight to each data point based on the variance of its fitted value. What is this? Essentially, this gives small weights to data points that have higher variances, which shrinks their squared residuals.
How do you deal with heteroskedasticity in regression?
How to Fix Heteroscedasticity
- Transform the dependent variable. One way to fix heteroscedasticity is to transform the dependent variable in some way.
- Redefine the dependent variable. Another way to fix heteroscedasticity is to redefine the dependent variable.
- Use weighted regression.
How can heteroscedasticity be corrected?
Correcting for Heteroscedasticity One way to correct for heteroscedasticity is to compute the weighted least squares (WLS) estimator using an hypothesized specification for the variance. Often this specification is one of the regressors or its square.