Testing for Heteroskedasticity and Predictive Failure in Linear Regression Models*
指出，进行Chow预测误差检验时需假设同方差性，并考察异方差性对该检验的影响，讨论异方差性检验的实施，特别关注含虚拟变量的情况，报告蒙特卡洛结果。
Abstract It is argued that, when researchers wish to carry out a Chow test of the significance of prediction errors, it is necessary to assume homoskedasticity because standard results on heteroskedasticity‐robust tests are not available. The effects of heteroskedasticity on the Chow prediction error test are examined. The implementation of tests for heteroskedasticity is discussed, with the case in which the regressors include dummy variables for prediction error tests receiving special attention. Monte Carlo results are reported.