Machine Learning Methods for Demand Estimation
Denis Nekipelov (),
Stephen Ryan () and
American Economic Review, 2015, vol. 105, issue 5, 481-85
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)
Note: DOI: 10.1257/aer.p20151021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10) Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to AEA members and institutional subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:aea:aecrev:v:105:y:2015:i:5:p:481-85
Ordering information: This journal article can be ordered from
Access Statistics for this article
American Economic Review is currently edited by Esther Duflo
More articles in American Economic Review from American Economic Association Contact information at EDIRC.
Bibliographic data for series maintained by Michael P. Albert ().