How do you test a Breusch-Pagan test?

We use the following steps to perform a Breusch-Pagan test:

  1. Fit the regression model.
  2. Calculate the squared residuals of the model.
  3. Fit a new regression model, using the squared residuals as the response values.
  4. Calculate the Chi-Square test statistic X2 as n*R2new where:

What is the null hypothesis for Breusch-Pagan test?

The null hypothesis for this test is that the error variances are all equal. The alternate hypothesis is that the error variances are not equal. More specifically, as Y increases, the variances increase (or decrease).

How do you test for heteroskedasticity?

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 the difference between breusch Pagan and White test?

The only different between White’s test and the Breusch-Pagan is that its auxiliary regression doesn’t include cross-terms or the original squared variables. Other than that, the steps are exactly the same.

What is Breusch-Pagan test for heteroskedasticity?

Breusch Pagan Test It is used to test for heteroskedasticity in a linear regression model and assumes that the error terms are normally distributed. It tests whether the variance of the errors from a regression is dependent on the values of the independent variables.

How do you check if a variable is normally distributed Stata?

In Stata, you can test normality by either graphical or numerical methods. The former include drawing a stem-and-leaf plot, scatterplot, box-plot, histogram, probability-probability (P-P) plot, and quantile-quantile (Q-Q) plot. The latter involve computing the Shapiro-Wilk, Shapiro-Francia, and Skewness/Kurtosis tests.

How do you fix heteroskedasticity in regression?

Use weighted 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. Essentially, this gives small weights to data points that have higher variances, which shrinks their squared residuals.