A Comment on “A Review of Student Test Properties in Condition of Multifactorial Linear Regression”
AbstractA 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.
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Bibliographic InfoArticle provided by Institute for Economic Forecasting in its journal Romanian Journal for Economic Forecasting.
Volume (Year): (2010)
Issue (Month): 3 (September)
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multifactorial classical normal regression; collinearity; multicollinearity; significance test; sampling distributions; power functions; Bayesian linear regression; prior information; posterior distributions;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- John Freebairn & Bill Griffiths, 2006. "Introduction," The Economic Record, The Economic Society of Australia, vol. 82(s1), pages S1-S1, 09.
- Pavelescu, Florin Marius, 2009. "A Review Of Student Test Properties In Condition Of Multifactorial Linear Regression," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 6(1), pages 63-75, March.
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