Breusch-Pagan 检验（Breusch-Pagan Test）：将OLS残差的平方对模型中的解释变量做回归的异方差性检验。
Breusch-Pagan-Godfrey (BPG)
The Breusch-Pagan-Godfrey test (see Breusch-Pagan, 1979, and Godfrey, 1978) is a Lagrange multiplier test of the null hypothesis of no heteroskedasticity against heteroskedasticity of the form , where  is a vector of independent variables. Usually this vector contains the regressors from the original least squares regression, but it is not necessary.

The test is performed by completing an auxiliary regression of the log of the original equation's squared residuals on . The explained sum of squares from this auxiliary regression is then divided by  to give an LM statistic, which follows a -distribution with degrees of freedom equal to the number of variables in  under the null hypothesis of no heteroskedasticity. Koenker (1981) suggested that a more easily computed statistic of Obs*R-squared (where  is from the auxiliary regression) be used. Koenker's statistic is also distributed as a  with degrees of freedom equal to the number of variables in . Along with these two statistics, EViews also quotes an F-statistic for a redundant variable test for the joint significance of the variables in  in the auxiliary regression.

As an example of a BPG test suppose we had an original equation of

log(m1) = c(1) + c(2)*log(ip) + c(3)*tb3

and we believed that there was heteroskedasticity in the residuals that depended on a function of LOG(IP) and TB3, then the following auxiliary regression could be performed

resid^2 = c(1) + c(2)*log(ip) + c(3)*tb3

Note that both the ARCH and White tests outlined below can be seen as Breusch-Pagan-Godfrey type tests, since both are auxiliary regressions of the log of squared residuals on a set of regressors and a constant.

你自己完全可以用手工生成残差序列，然后按照帮助提示，建立残差的回归方程，进行该项检验

对我有帮助哈！

非常有帮助。
顺便问一下，longitudinal data里，不是一般假设error项是不能与X项有相关性的么？这是需要检验heteroskedasticity的原因么？

不懂。。

是啊，截面数据非常容易产生规模效应scale effect，所以异方差性是截面数据要着重考虑的东西