Perform White's test for heteroscedasticity

Implements White's test on a fitted linear model.

Usage

performWhiteTest(model, data, cross_products = TRUE, max_interactions = 10)

Details

An auxiliary regression of \(e^2\) on all regressors, their squares and cross-products produces \(R^2\). The statistic \(n R^2\) follows a chi-square distribution with degrees of freedom equal to the number of regressors in the auxiliary model.

Arguments

model

an object of class lm.

data

Data frame used to fit model.

cross_products

Logical. Include cross-product terms in the auxiliary regression?

max_interactions

Maximum number of cross-product terms admitted to the auxiliary regression. Guards the auxiliary design against growing quadratically with the number of regressors.

Value

An object of class htest containing the test statistic, p-value and degrees of freedom.

References

White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. doi:10.2307/1912934

Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson.

Examples

 data(mtcars)
 m <- lm(mpg ~ wt + qsec, data = mtcars)
 performWhiteTest(m, mtcars)
#> [INFO] Running White test
#> [INFO] White test completed: statistic = 11.8225 df = 5 p = 0.0373
#> 
#> 	White's test for heteroscedasticity
#> 
#> data:  m
#> X-squared = 11.822, df = 5, p-value = 0.0373
#> alternative hypothesis: heteroscedasticity present
#>