Validating linear restrictions in linear regression models with general error structure

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.