Department of Economics, Univ. of Washington
305 Savery Hall, Box 353330
Seattle, WA 98195-3330
NBER Working Papers and Publications
|February 2015||Demand Estimation with Machine Learning and Model Combination|
with Patrick Bajari, Denis Nekipelov, Stephen P. Ryan: w20955
We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. We derive novel asymptotic properties for several of these models. To improve out-of-sample prediction accuracy and obtain parametric rates of convergence, we propose a method of combining the underlying models via linear regression. Our method has several appealing features: it is robust to a large number of potentially-collinear regressors; it scales easily to very large data sets; the machine learning methods combine model selection and estimation; and the method can flexibly approximate arbitrary non-linear functions, even when the set of regressors is high dimensional and we also allow for fixed effects. We illustrate our method using a standard scanner pan...