What does the breusch-Pagan test do?
What is the Breusch-Pagan Test? The Breusch-Pagan test is used to determine whether or not heteroscedasticity is present in a regression model. The test uses the following null and alternative hypotheses: Null Hypothesis (H0): Homoscedasticity is present (the residuals are distributed with equal variance)
What is heteroskedasticity in regression?
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).
What is Breusch-Pagan test in R?
The Breusch-Pagan test fits a linear regression model to the residuals of a linear regression model (by default the same explanatory variables are taken as in the main regression model) and rejects if too much of the variance is explained by the additional explanatory variables.
Why is heteroscedasticity bad?
What Problems Does Heteroscedasticity Cause? Heteroscedasticity tends to produce p-values that are smaller than they should be. This effect occurs because heteroscedasticity increases the variance of the coefficient estimates but the OLS procedure does not detect this increase.
How to test for heteroscedasticity in a model?
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.
Which is the Wald function for heteroskedasticity?
For a heteroskedasticity robust F test we perform a Wald test using the waldtest function, which is also contained in the lmtest package. It can be used in a similar way as the anova function, i.e., it uses the output of the restricted and unrestricted model and the robust variance-covariance matrix as argument vcov.
Why is there no heteroscedasticity in linear regression?
One of the important assumptions of linear regression is that, there should be no heteroscedasticity of residuals. In simpler terms, this means that the variance of residuals should not increase with fitted values of response variable.
What happens when the assumption of heteroscedasticity is violated?
When this assumption is violated, the problem is known as 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.