Machine Learning Methods for Demand Estimation.
We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. To improve out-of-sample prediction accuracy, we propose a method of combining the underlying models via linear regression. Our method is robust to a large number of regre...
| Publicado en: | American Economic Review Vol. 105; no. 5; pp. 481 - 486 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
American Economic Association
May2015
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. To improve out-of-sample prediction accuracy, we propose a method of combining the underlying models via linear regression. Our method is robust to a large number of regressors; scales easily to very large data sets; combines model selection and estimation; and can flexibly approximate arbitrary non-linear functions. We illustrate our method using a standard scanner panel data set and find that our estimates are considerably more accurate in out-of-sample predictions of demand than some commonly used alternatives. |
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