American Economic Review
ISSN 0002-8282 (Print) | ISSN 1944-7981 (Online)
Machine Learning Methods for Demand Estimation
American Economic Review
vol. 105,
no. 5, May 2015
(pp. 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.Citation
Bajari, Patrick, Denis Nekipelov, Stephen P. Ryan, and Miaoyu Yang. 2015. "Machine Learning Methods for Demand Estimation." American Economic Review 105 (5): 481–85. DOI: 10.1257/aer.p20151021Additional Materials
JEL Classification
- C20 Single Equation Models; Single Variables: General
- C52 Model Evaluation, Validation, and Selection
- C55 Large Data Sets: Modeling and Analysis
- D12 Consumer Economics: Empirical Analysis
- D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness