The Superiority of Simple Alternatives to Regression for Social Science Predictions

Jason Dana and Robyn M. Dawes

Journal of Educational and Behavioral Statistics, 2004, vol. 29, issue 3, 317-331

Abstract: Some simple, nonoptimized coefficients (e.g., correlation weights, equal weights) were pitted against regression in extensive prediction competitions. After drawing calibration samples from large supersets of real and synthetic data, the researchers observed which set of sample-derived coefficients made the best predictions when applied back to the superset. When adjusted R from the calibration sample was

Keywords: forecasting; improper linear models; prediction (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:29:y:2004:i:3:p:317-331

DOI: 10.3102/10769986029003317

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