A Locally Most Mean Powerful Based Score Test for ARCH and GARCH Regression Disturbances
针对线性回归模型中ARCH和GARCH扰动检验的单侧性质，构造了基于得分之和的新检验，蒙特卡洛实验表明其功效优于拉格朗日乘子检验，且渐近临界值更准确。
This paper considers the twin problems of testing for ARCH and GARCH disturbances in the linear regression model.A feature of these testing problems, ignored by the standard Lagrange multiplier test, is that they are one-sided in nature.A test which exploits this one-sided aspect is constructed based on the sum of the scores.Its small-sample size and power properties under both normal and leptokurtic disturbances are investigated via a Monte Carlo experiment.The results indicate that the new test typically has superior power to two versions of the Lagrange multiplier test and possibly also more accurate asymptotic critical values.