# How do you test for heteroskedasticity in a White test?

## How do you test for heteroskedasticity in a White test?

Follow these five steps to perform a White test:

1. Estimate your model using OLS:
2. Obtain the predicted Y values after estimating your model.
3. Estimate the model using OLS:
4. Retain the R-squared value from this regression:
5. Calculate the F-statistic or the chi-squared statistic:

What does the White test tell us?

In statistics, the White test is a statistical test that establishes whether the variance of the errors in a regression model is constant: that is for homoskedasticity. This test, and an estimator for heteroscedasticity-consistent standard errors, were proposed by Halbert White in 1980.

### What is the best test for heteroskedasticity?

There are a couple of ways to test for heteroskedasticity.

• Visual Test. The easiest way to test for heteroskedasticity is to get a good look at your data.
• Breusch-Pagan Test. The Breusch-Pagan test is a quick and dirty way to determine statistically whether your data is heteroskedastic.
• White’s Test.
• The Takeaways.

How do you know if you have 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.

#### Why do we use the white test?

White’s test is used to test for heteroscedastic (“differently dispersed”) errors in regression analysis. A graph showing heteroscedasticity; the White test is used to identify heteroscedastic errors in regression analysis. The null hypothesis for White’s test is that the variances for the errors are equal. 