Perform Breusch-Pagan test for heteroscedasticity¶
Source: man/performBPTest.Rd · Help: ?performBPTest
Implements the classical Breusch-Pagan (1979) test on a fitted linear model, in which the scaled squared residuals are regressed on the original regressors.
Usage¶
Arguments¶
model- an object of class
lm. data- data frame used to fit
model. WhenNULL, the default, the model frame ofmodelis used. It holds the terms of the model rather than its variables (log(x)rather thanx), so a model that transforms a variable needsdata, as does a model with anoffset()term, and so may a fit whoseweightsoroffsetargument is a column of the data, which the model frame holds as(weights)or(offset).
Value¶
An object of class htest containing the test statistic, p-value and degrees of freedom.
Details¶
The test statistic is half the explained sum of squares from regressing the scaled squared residuals \(e_i^2/\hat{\sigma}^2 - 1\) (with \(\hat{\sigma}^2 = \sum e_i^2 / n\)) on the explanatory variables. Under the null hypothesis of homoscedasticity and normal disturbances it follows a chi-square distribution with degrees of freedom equal to the number of regressors, matching lmtest::bptest(..., studentize = FALSE). For the studentized \(n R^2\) form that drops the normality assumption use performKoenkerTest.
Deprecated name¶
performBreuschPaganTest() is deprecated since 0.14.0. It was the same function as performBPTest() under a second name; it still works, returns what performBPTest() returns and warns. Use performBPTest().
Weighted fits¶
On a fit with weights the test is computed from the Pearson residuals \(\sqrt{w_i}\, e_i\), so it tests whether the weights are adequate; see the section on weighted fits in performKoenkerTest(), which also covers weights estimated by fitWLS().
References¶
Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47(5), 1287–1294. doi:10.2307/1911963
Koenker, R. (1981). A note on studentizing a test for heteroscedasticity. Journal of Econometrics, 17(1), 107–112. doi:10.1016/0304-4076(81)90062-2