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Machine Learning Methods for Demand Estimation

Patrick Bajari, Denis Nekipelov (), Stephen Ryan () and Miaoyu Yang

American Economic Review, 2015, vol. 105, issue 5, 481-85

Abstract: 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.

JEL-codes: C20 C52 C55 D12 D83 (search for similar items in EconPapers)
Date: 2015
Note: DOI: 10.1257/aer.p20151021
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Handle: RePEc:aea:aecrev:v:105:y:2015:i:5:p:481-85