The white test helps detect heteroscedasticity in the residuals from "summary" of Introduction to Econometrics by Christopher Dougherty
The White test is a diagnostic tool used in econometrics to detect heteroscedasticity in the residuals of a regression model. Heteroscedasticity refers to the phenomenon where the variance of the error term is not constant across all levels of the independent variables. This violates one of the key assumptions of classical linear regression models, namely that the error term is homoscedastic, or has constant variance. To understand how the White test works, we first need to consider how heteroscedasticity can affect the residuals of a regression model. When the variance of the error term is not constant, it can lead to biased and inefficient estimates of the coefficients in the model. This can result in misleading conclusions about the relationships between the variables under study. The White test is designed to detect this issue by examining the residuals of the regression model. It does this by regressing the squared residuals on the independent variables in the model. If there is heteroscedasticity present, this regression will show a significant relationship between the squared residuals and the independent variables. This indicates that the variance of the error term is not constant and that heteroscedasticity is present in the model.- Researchers can diagnose the presence of heteroscedasticity in their regression models and take appropriate steps to address it. This may involve using heteroscedasticity-robust standard errors or transforming the data to achieve constant variance in the error term. Overall, the White test is a valuable tool for ensuring the reliability and validity of regression analyses in econometrics.
