Bootstrap methods for heteroskedastic regression models: evidence on estimation and testing
通过蒙特卡洛模拟，研究了四种Bootstrap方案在线性异方差模型中的有限样本表现，发现加权Bootstrap能有效估计最小二乘估计量的方差，且Bootstrap Breusch-Pagan检验对不利回归设计具有稳健性。
This paper uses Monte Carlo simulation analysis to study the finite-sample behavior of bootstrap estimators and tests in the linear heteroskedastic model. We consider four different bootstrapping schemes, three of them specifically tailored to handle heteroskedasticity. Our results show that weighted bootstrap methods can be successfully used to estimate the variances of the least squares estimators of the linear parameters both under normality and under nonnormality. Simulation results are also given comparing the size and power of the bootstrapped Breusch-Pagan test with that of the original test and of Bartlett and Edgeworth-corrected tests. The bootstrap test was found to be robust against unfavorable regression designs.