Machine Learning Methods for Demand Estimation
综述并应用统计和计算机科学中的多种技术进行需求估计，提出一种通过线性回归组合底层模型的方法，在扫描面板数据上比常用方法更准确地预测需求。
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.