IDEAS home Printed from https://ideas.repec.org/a/ces/ifosdt/v71y2018i07p50-53.html
   My bibliography  Save this article

Machine Learning in Economic Research

Author

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

Abstract

In empirical economic research, the number of publications using methods of machine learning is increasing, albeit some scepticism prevails. One point of criticism is that although machine learning is suitable for forecasts, it is not able to identify causal relationships. However, in recent years research has dealt more thoroughly with this problem and many advances have been made. Machine learning thus has the potential to become more important in economic research in the future.

Suggested Citation

  • Matthias Huber & Simone Schüller & Marc Stöckli & Klaus Wohlrabe, 2018. "Machine Learning in Economic Research," 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
    as

    Download full text from publisher

    File URL: https://www.ifo.de/DocDL/sd-2018-07-huber-etal-maschinelles-lernen-2018-04-12.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. R. Lehmann & K. Wohlrabe, 2016. "Looking into the black box of boosting: the case of Germany," Applied Economics Letters, Taylor & Francis Journals, vol. 23(17), pages 1229-1233, November.
    2. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 3-30, Spring.
    3. Klaus Wohlrabe & Teresa Buchen, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area and Germany," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 231-242, July.
    4. Leif Anders Thorsrud, 2016. "Nowcasting using news topics Big Data versus big bank," Working Papers No 6/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    2. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    3. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    4. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    5. Robert Lehmann & Klaus Wohlrabe, 2017. "Boosting and regional economic forecasting: the case of Germany," Letters in Spatial and Resource Sciences, Springer, vol. 10(2), pages 161-175, July.
    6. Robert Lehmann & Klaus Wohlrabe, 2016. "Boosting and Forecasting German Industrial Output: What Does a Closer Look at the Details Tell Us?," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 69(03), pages 30-33, February.
    7. Guilherme Lindenmeyer & Pedro Pablo Skorin & Hudson da Silva Torrent, 2021. "Using boosting for forecasting electric energy consumption during a recession: a case study for the Brazilian State Rio Grande do Sul," Letters in Spatial and Resource Sciences, Springer, vol. 14(2), pages 111-128, August.
    8. Pierdzioch Christian & Gupta Rangan, 2020. "Uncertainty and Forecasts of U.S. Recessions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(4), pages 1-20, September.
    9. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
    10. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    11. Czajkowski, Mikołaj & Zagórska, Katarzyna & Letki, Natalia & Tryjanowski, Piotr & Wąs, Adam, 2021. "Drivers of farmers’ willingness to adopt extensive farming practices in a globally important bird area," Land Use Policy, Elsevier, vol. 107(C).
    12. KAMKOUM, Arnaud Cedric, 2023. "The Federal Reserve’s Response to the Global Financial Crisis and its Effects: An Interrupted Time-Series Analysis of the Impact of its Quantitative Easing Programs," Thesis Commons d7pvg, Center for Open Science.
    13. Burt S. Barnow & Jeffrey Smith, 2015. "Employment and Training Programs," NBER Chapters, in: Economics of Means-Tested Transfer Programs in the United States, Volume 2, pages 127-234, National Bureau of Economic Research, Inc.
    14. Christopher Conlon & Julie Holland Mortimer, 2021. "Empirical properties of diversion ratios," RAND Journal of Economics, RAND Corporation, vol. 52(4), pages 693-726, December.
    15. Christopher J. Ruhm, 2019. "Shackling the Identification Police?," Southern Economic Journal, John Wiley & Sons, vol. 85(4), pages 1016-1026, April.
    16. Christos Genakos & Andreas Lamprinidis & James Walker, 2023. "Evaluating merger effects," CEP Discussion Papers dp1921, Centre for Economic Performance, LSE.
    17. Katherine Sawyer & Kathleen Gallagher Cunningham & William Reed, 2017. "The Role of External Support in Civil War Termination," Journal of Conflict Resolution, Peace Science Society (International), vol. 61(6), pages 1174-1202, July.
    18. Koki Arai, 2017. "Ex-post examination of mergers: effects on retail prices," Asia-Pacific Journal of Accounting & Economics, Taylor & Francis Journals, vol. 24(1-2), pages 145-162, April.
    19. Elliott Ash & W. Bentley MacLeod, 2015. "Intrinsic Motivation in Public Service: Theory and Evidence from State Supreme Courts," Journal of Law and Economics, University of Chicago Press, vol. 58(4).
    20. Annette N. Brown & Drew B. Cameron & Benjamin D. K. Wood, 2014. "Quality evidence for policymaking: I'll believe it when I see the replication," Journal of Development Effectiveness, Taylor & Francis Journals, vol. 6(3), pages 215-235, September.

    More about this item

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ces:ifosdt:v:71:y:2018:i:07:p:50-53. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/ifooode.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.