Machine Learning Methods for Demand Estimation

2015/05/01 by Patrick Bajari, Denis Nekipelov, Stephen P. Ryan +2 · 11 citations
Business, Management and Accounting · Decision Sciences · #Consumer Market Behavior and Pricing #Forecasting Techniques and Applications #Advanced Statistical Process Monitoring

paper · doi:10.1257/aer.p20151021

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.

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