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The Error Rate of Tests for Regression Coincidence and Parallelism Under Unknown Multiplicative Heteroscedasticity
Authors:Timothy G. Gregoire
Abstract:The usual F test of regression coincidence, which is appropriate under a homoscedastic model, is examined under a multiplicatively heteroscedastic model. The departure of the test from its nominal level is slight when the sample of explanatory variables is symmetric, but may be substantially inflated when the sample has positive skew. Conversely, the nominal level may be slightly depressed when the sample has negative skew. The size of the perturbation from the nominal level depends on the degree of heteroscedasticity, however its effect is more pronounced with positively skewed samples. Similar trends are evident for the usual F test of regression parallelism. There is no apparent pattern to the discrepancy of the level of the test with regard to the data which would permit empirical researchers to adjust their results.
Keywords:Regression modeling  Ordinary least squares  Significance level
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