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Interpreting Results of Demand Estimation from Machine Learning Models

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  • Green, Gareth
  • Richards, Timothy

Abstract

There is developing interest in the application of Machine Learning Models (MLM) to estimation problems in economics. MLM may be particularly well suited to applications in retail, health care, energy, finance or for web based businesses where large amounts of data are available to help make better decisions and better understand consumer behavior. There are three reasons economists may want to adopt new MLM tools. First is the size of available data sets. Second, these new data sets have many potential predictors where domain knowledge may not be helpful in distinguishing which available data are most relevant. Third, larger data sets allow for modeling more complex relationships than the standard linear model, which is what MLM are able to capture.

Suggested Citation

  • Green, Gareth & Richards, Timothy, 2016. "Interpreting Results of Demand Estimation from Machine Learning Models," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236147, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea16:236147
    DOI: 10.22004/ag.econ.236147
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    References listed on IDEAS

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    1. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    2. Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Demand Estimation with Machine Learning and Model Combination," NBER Working Papers 20955, National Bureau of Economic Research, Inc.
    3. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    4. Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
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    Cited by:

    1. David Mayer-Foulkes, 2018. "Efficient Urbanization for Mexican Development," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(10), pages 1-1, October.

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    Keywords

    Consumer/Household Economics; Demand and Price Analysis;

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