This article considers the twin problems of testing for autoregressive conditional heteroscedas- ticity (ARCH) and generalized ARCH disturbances in the linear regression model. A feature of these testing problems, ignored by the standard Lagrange multiplier test, is that they are onesided in nature. A test that exploits this one-sided aspect is constructed based on the sum of the scores. The small-sample-size and power properties of two versions of this test under both normal and leptokurtic disturbances are investigated via a Monte Carlo experiment. The results indicate that both versions of the new test typically have superior power to two versions of the Lagrange multiplier test and possibly also more accurate asymptotic critical values.
- Autoregressive conditional heteroscedasticity
- Generalized autoregressive conditional heteroscedasticity
- Lagrange multiplier test
- Leptokurtic regression disturbances
- Monte Carlo experiment