A Comment on “A Review of Student Test Properties in Condition of Multifactorial Linear Regression”

Eric Eisenstat

Journal for Economic Forecasting, 2010, issue 3, 53-73

Abstract: A recent article (Pavelescu, 2009) proposes a correction to the conventional student-t test of significance in linear regression models, but offers no formal description of its properties. This comment formally characterizes the sampling properties of the corrected student-t statistic. In application to multifactorial regressions, it turns out that the corrected student-t statistic is not ancillary – its sampling distribution depends on unknown nuisance parameters.Therefore, it is impossible to reasonably compute critical values and operatively designate a rejection criterion using such a test statistic, which makes the proposed testing procedure impractical. Some suggestions regarding the search for similar testing procedures are proposed and a Bayesian alternative is further discussed.

Keywords: multifactorial classical normal regression; collinearity; multicollinearity; significance test; sampling distributions; power functions; Bayesian linear regression; prior information; posterior distributions (search for similar items in EconPapers)
JEL-codes: C11 C12 (search for similar items in EconPapers)
Date: 2010
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