| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w20955 |
| 来源ID | Working Paper 20955 |
| Demand Estimation with Machine Learning and Model Combination | |
| Patrick Bajari; Denis Nekipelov; Stephen P. Ryan; Miaoyu Yang | |
| 发表日期 | 2015-02-16 |
| 出版年 | 2015 |
| 语种 | 英语 |
| 摘要 | 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 panel data set to estimate promotional lift and find that our estimates are considerably more accurate in out of sample predictions of demand than some commonly used alternatives. While demand estimation is our motivating application, these methods are likely to be useful in other microeconometric problems. |
| 主题 | Econometrics ; Estimation Methods |
| URL | https://www.nber.org/papers/w20955 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/578630 |
| 推荐引用方式 GB/T 7714 | Patrick Bajari,Denis Nekipelov,Stephen P. Ryan,et al. Demand Estimation with Machine Learning and Model Combination. 2015. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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