A new method for testing linear restrictions in linear regression models is suggested.
It allows to validate the linear restriction, up to a specified approximation
error and with a specified error probability. The test relies on asymptotic normality
of the test statistic, and therefore normality of the errors in the regression model is
not required. In a simulation study the performance of the suggested method for
model selection purposes, as compared to standard model selection criteria and the
t-test, is examined. As an illustration we analyze the US college spending data from
1994.
«A new method for testing linear restrictions in linear regression models is suggested.
It allows to validate the linear restriction, up to a specified approximation
error and with a specified error probability. The test relies on asymptotic normality
of the test statistic, and therefore normality of the errors in the regression model is
not required. In a simulation study the performance of the suggested method for
model selection purposes, as compared to standard model selection criteria a...
»