Replication data for: Machine Learning Methods for Demand Estimation
Principal Investigator(s): View help for Principal Investigator(s) Patrick Bajari; Denis Nekipelov; Stephen P. Ryan; Miaoyu Yang
Version: View help for Version V1
| Name | File Type | Size | Last Modified |
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| P2015_1021_data | 10/12/2019 10:19:AM | ||
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text/plain | 14.6 KB | 10/12/2019 06:19:AM |
Project Citation:
Bajari, Patrick, Nekipelov, Denis, Ryan, Stephen P., and Yang, Miaoyu. Replication data for: Machine Learning Methods for Demand Estimation. Nashville, TN: American Economic Association [publisher], 2015. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-12. https://doi.org/10.3886/E113366V1
Project Description
Summary:
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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.
Scope of Project
JEL Classification:
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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
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
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