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.