Machine Learning: An Applied Econometric Approach.
Machines are increasingly doing 'intelligent' things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the presence y of a face from pixels x. This similarity to econometrics raises questions: How do these new empirical to...
| Publicado en: | Journal of Economic Perspectives Vol. 31; no. 2; pp. 87 - 107 |
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| Autores principales: | , |
| Formato: | Artículo |
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American Economic Association
Spring2017
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| 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=122833479&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 122833479 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 08953309 JEC jtl: Journal of Economic Perspectives issn: 08953309 maglogo: N pubinfo: dt: Spring2017 vid: 31 iid: 2 pid: 22 pub: American Economic Association artinfo: ui: 122833479 10.1257/jep.31.2.87 ppf: 87 ppct: 20 formats: tig: atl: Machine Learning: An Applied Econometric Approach. aug: au: Mullainathan, Sendhil Spiess, Jann affil: Sendhil Mullainathan is the Robert C. Waggoner Professor of Economics, Harvard University, Cambridge, Massachusetts. Jann Spiess is a PhD candidate in Economics, Harvard University, Cambridge, Massachusetts. su: Econometrics Artificial intelligence Empirical research Economists Machine learning Algorithms sug: subj: Econometrics Artificial intelligence Empirical research Economists Machine learning Algorithms ab: Machines are increasingly doing 'intelligent' things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the presence y of a face from pixels x. This similarity to econometrics raises questions: How do these new empirical tools fit with what we know? As empirical economists, how can we use them? We present a way of thinking about machine learning that gives it its own place in the econometric toolbox. Machine learning not only provides new tools, it solves a different problem. Specifically, machine learning revolves around the problem of prediction, while many economic applications revolve around parameter estimation. So applying machine learning to economics requires finding relevant tasks. Machine learning algorithms are now technically easy to use: you can download convenient packages in R or Python. This also raises the risk that the algorithms are applied naively or their output is misinterpreted. We hope to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble-and thus where they can be most usefully applied. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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