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Maschinelles Lernen in der ökonomischen Forschung

Author

Listed:
  • Matthias Huber
  • Simone Schüller
  • Marc Stöckli
  • Klaus Wohlrabe

Abstract

In der empirischen Wirtschaftsforschung steigt die Anzahl der Publikationen, die mit Methoden des maschinellen Lernens arbeiten. Dennoch scheint eine gewisse Skepsis zu bestehen. Ein Kritikpunkt ist, dass sich maschinelles Lernen zwar für Vorhersagen eignet, aber keine kausalen Zusammenhänge identifizieren kann. In den vergangenen Jahren hat sich die Forschung jedoch verstärkt mit diesem Problem auseinandergesetzt, und es wurden zahlreiche Fortschritte erzielt. Maschinelles Lernen hat daher das Potenzial, in Zukunft in der Wirtschaftsforschung an Bedeutung zu gewinnen.

Suggested Citation

  • Matthias Huber & Simone Schüller & Marc Stöckli & Klaus Wohlrabe, 2018. "Maschinelles Lernen in der ökonomischen Forschung," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(07), pages 50-53, April.
    • Handle: RePEc:ces:ifosdt:v:71:y:2018:i:07:p:50-53
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    References listed on IDEAS

    as
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    4. Athey, Susan & Imbens, Guido W. & Wager, Stefan, 2016. "Efficient Inference of Average Treatment Effects in High Dimensions via Approximate Residual Balancing," Research Papers 3408, Stanford University, Graduate School of Business.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Wirtschaftsinformatik; Prognoseverfahren; Algorithmus;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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