A Note on the Use of R-squared in Model Selection
AbstractThe use of R-squared in Model Selection is a common practice in econometrics. The rationale is that the statistic produces a consistent estimator of the true coefficient of determination for the underlying data while taking into consideration the number of variables involved in the model. This pursuit of parsimony comes with a cost: The researcher has no control over the error probabilities of the statistic. Alternative measures of goodness of fit, such as the Schwarz Information Criterion, provide only a marginal improvement to the problem. The F-Test under the Neyman-Pearson testing framework will provide the best alternative for model selection criteria.
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Bibliographic InfoPaper provided by Department of Economics, College of William and Mary in its series Working Papers with number 62.
Length: 14 pages
Date of creation: 21 Oct 2007
Date of revision:
Adjusted R squared; Schwarz Information Criterion BIC; Neyman-Pearson Testing; Nonsense Correlations;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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