What test do you use to check for heteroscedasticity?
F Test. F Test for heteroskedasticity under the assumption that the errors are independent and identically distributed (i.i.d.). You can perform the test using the fitted values of the model, the predictors in the model and a subset of the independent variables.
Do you think that if heteroscedasticity is present the conventional T and F tests are invalid Why?
The correct option is False. In the presence of heteroscedasticity, the variance of the OLS estimator changes and is larger than in the absence of heteroscedasticity implies the conventional t and F test measures will be insignificant and do not give valid results.
How is heteroskedasticity detected?
One informal way of detecting heteroskedasticity is by creating a residual plot where you plot the least squares residuals against the explanatory variable or ˆy if it’s a multiple regression. If there is an evident pattern in the plot, then heteroskedasticity is present.
Why is the White test for heteroskedasticity general?
White test for Heteroskedasticity is general because it do not rely on the normality assumptions and it is also easy to implement. Because of the generality of White’s test, it may identify the specification bias too. Both White’s test and the Breusch-Pagan test are based on the residuals of the fitted model.
How does the Breusch-Pagan test check for heteroscedasticity?
The Breusch-Pagan test only checks for the linear form of heteroskedasticity i.e. it models the error variance as σ i 2 = σ 2 h ( z i ′ α) where z i is a vector of your independent variables. It tests H 0: α = 0 versus H a: α ≠ 0.
Can a non linear form of heteroskedasticity be tested?
In this default form, the test does not work well for non-linear forms of heteroskedasticity, such as the hourglass shape we saw before (where error variances got larger as X got more extreme in either direction).
How does the White test for homoscedasticity work?
Both White’s test and the Breusch-Pagan test are based on the residuals of the fitted model. To test the assumption of homoscedasticity, one can use auxiliary regression analysis by regressing the squared residuals from the original model on the set of original regressors, the cross-products of the regressors, and the squared regressors.