Machine Learning Methods for Demand Estimation

We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. To improve out-of-sample prediction accuracy, we propose a method of combining the underlying models via linear regression. Our method is robust to a large number of regre...

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Bibliographic Details
Published in:The American economic review 2015-05, Vol.105 (5), p.481-485
Main Authors: Bajari, Patrick, Nekipelov, Denis, Ryan, Stephen P., Yang, Miaoyu
Format: Article
Language:English
Subjects:
Applied econometrics
Applied economics
Artificial intelligence
Computer science
Datasets
Economic modeling
Economic models
Economic statistics
Economic theory
Economics
Estimating techniques
Estimation methods
Learning
Machine learning
MACHINE LEARNING METHODS IN ECONOMICS AND ECONOMETRICS
Modeling
Open source software
Regression analysis
Scanners
Software packages
Studies
Support vector machines
Trees
Variables
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Description
Summary:We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. To improve out-of-sample prediction accuracy, we propose a method of combining the underlying models via linear regression. Our method is robust to a large number of regressors; scales easily to very large data sets; combines model selection and estimation; and can flexibly approximate arbitrary non-linear functions. We illustrate our method using a standard scanner panel data set and find that our estimates are considerably more accurate in out-of-sample predictions of demand than some commonly used alternatives.
ISSN:0002-8282
1944-7981
DOI:10.1257/aer.p20151021