An exact, unified distributional characterization of statistics used to test linear hypotheses in simple regression models
The Wald, likelihood ratio and Lagrange multiplier test statistics are commonly used to test linear restrictions in regression models. It is shown that for testing these restrictions in the classical regression model, the exact densities of these test statistics are special cases of the generalized beta distribution introduced by McDonald (1984); McDonald and Xu (1995a). This unified derivation provides a method by which one can derive small sample critical values for each test. These results may be indicative of the behavior of such test statistics in more general settings, and are useful in visualizing how each statistic changes with different parameter values in the simple regression model. For example, the results suggest that Wald tests may severely underreject the null hypothesis when the sample size is small or a large number of restrictions are tested.
|Date of creation:||20 May 2010|
|Contact details of provider:|| Postal: Ludwigstraße 33, D-80539 Munich, Germany|
Web page: https://mpra.ub.uni-muenchen.de
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:22841. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter)
If references are entirely missing, you can add them using this form.