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Machine Learning: An Applied Econometric Approach

Author

Listed:
  • Sendhil Mullainathan
  • Jann Spiess

Abstract

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.

Suggested Citation

  • Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
  • Handle: RePEc:aea:jecper:v:31:y:2017:i:2:p:87-106
    Note: DOI: 10.1257/jep.31.2.87
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    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Sam Watson’s journal round-up for 12th June 2017
      by Sam Watson in The Academic Health Economists' Blog on 2017-06-12 16:00:00

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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    Cited by:

    1. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    2. Jens Ludwig & Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning Tests for Effects on Multiple Outcomes," Papers 1707.01473, arXiv.org.
    3. Carlos León & Fabio Ortega, 2018. "Nowcasting economic activity with electronic payments data: A predictive modeling approach," Borradores de Economia 1037, Banco de la Republica de Colombia.
    4. John Gathergood & Neale Mahoney & Neil Stewart & Joerg Weber, 2017. "How Do Individuals Repay Their Debt? The Balance-Matching Heuristic," NBER Working Papers 24161, National Bureau of Economic Research, Inc.
    5. Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," IZA Discussion Papers 10961, Institute for the Study of Labor (IZA).
    6. Lionel Roger, 2018. "Blinded by the Light? Heterogeneity in the Luminosity-Growth Nexus and the African Growth Miracle," Discussion Papers 2018-04, University of Nottingham, CREDIT.
    7. Paolo Brunori & Paul Hufe & Daniel Gerszon Mahler, 2017. "The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees," Working Papers - Economics wp2017_18.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    8. Monica Andini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Viola Salvestrini, 2017. "Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy," Temi di discussione (Economic working papers) 1158, Bank of Italy, Economic Research and International Relations Area.
    9. repec:kap:poprpr:v:37:y:2018:i:1:d:10.1007_s11113-017-9450-4 is not listed on IDEAS
    10. repec:nbr:nberch:14009 is not listed on IDEAS
    11. Fritz Schiltz & Chiara Masci & Tommaso Agasisti & Daniel Horn, 2017. "Using Machine Learning To Model Interaction Effects In Education: A Graphical Approach," Budapest Working Papers on the Labour Market 1704, Institute of Economics, Centre for Economic and Regional Studies, Hungarian Academy of Sciences.
    12. Potnuru Kishen Suraj & Ankesh Gupta & Makkunda Sharma & Sourabh Bikash Paul & Subhashis Banerjee, 2017. "On monitoring development using high resolution satellite images," Papers 1712.02282, arXiv.org, revised Dec 2017.
    13. repec:bis:bisifc:46-26 is not listed on IDEAS
    14. repec:nbr:nberch:14024 is not listed on IDEAS
    15. Jeannine Bailliu & Xinfen Han & Mark Kruger & Yu-Hsien Liu & Sri Thanabalasingam, 2018. "Can Media and Text Analytics Provide Insights into Labour Market Conditions in China?," Staff Working Papers 18-12, Bank of Canada.

    More about this item

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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