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 |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=102579638&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 102579638 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00028282 AER jtl: American Economic Review issn: 00028282 maglogo: N pubinfo: dt: May2015 vid: 105 iid: 5 pid: 22 pub: American Economic Association artinfo: ui: 102579638 10.1257/aer.p20151021 ppf: 481 ppct: 5 formats: tig: atl: Machine Learning Methods for Demand Estimation. aug: au: Bajari, Patrick Nekipelov, Denis Ryan, Stephen P. Yang, Miaoyu affil: Department of Economics, University of Washington, 331 Savery Hall, Seattle, WA 98195, and NBER (e-mail: ) Department of Economics, University of Virginia, 254 Monroe Hall, Charlottesville, VA 22904 (e-mail: ) Department of Economics, University of Texas at Austin, 2225 Speedway Stop C3100, BRB 3.134D, Austin, TX 78712, and NBER (e-mail: ) Department of Economics, University of Washington, 331 Savery Hall, Seattle, WA 98195 (e-mail: ) su: Economic demand Machine learning Estimation theory Mathematical models Demand function Big data Regression analysis sug: subj: Economic demand Machine learning Estimation theory Mathematical models Demand function Big data Regression analysis ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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