Measuring overfitting and mispecification in nonlinear models
AbstractWe start by proposing a new measure of overfitting expressed on the untransformed scale of the dependent variable, which is generally the scale of interest to the analyst.We then show that with nonlinear models shrinkage due to overfitting gets confounded by shrinkage—or expansion— arising from model misspecification. Out-of-sample predictive calibration can in fact be expressed as in-sample calibration times 1 minus this new measure of overfitting. We finally argue that re-calibration should be performed on the scale of interest and provide both a simulation study and a real-data illustration based on health care expenditure data.
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Bibliographic InfoPaper provided by HEDG, c/o Department of Economics, University of York in its series Health, Econometrics and Data Group (HEDG) Working Papers with number 11/25.
Date of creation: Aug 2011
Date of revision:
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overfitting; shrinkage; misspecification; forecasting; health care expenditure;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-08-15 (All new papers)
- NEP-ECM-2011-08-15 (Econometrics)
- NEP-FOR-2011-08-15 (Forecasting)
- NEP-HEA-2011-08-15 (Health Economics)
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