This paper considers testing for autoregressive conditional heteroskedasticity and generalized autoregressive conditional heteroskedasticity disturbances in the linear regression model. These testing problems are one-sided in nature; a fact ignored by the Lagrange multiplier test. A test that exploits this one-sided aspect is constructed based on the sum of the scores. The size and power properties of two versions of this test under normal and leptokurtic disturbances are investigated via a Monte Carlo experiment. The results indicate that both version s of the new test typically have superior power to two versions of the Lagrange multiplier test and possibly also more accurate asymptotic critical values.
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Volume (Year): 11 (1993) Issue (Month): 1 (January) Pages: 17-27 Download reference. The following formats are available: HTML
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