Demand Analysis with Many Prices: Methods and Application

多种价格的需求分析：方法与应用

基本信息

批准号：
1757140
负责人：
Whitney Newey
金额：
$ 18.37万
依托单位：
National Bureau of Economic Research Inc
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-01 至 2023-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1757140&HistoricalAwards=false
关键词：
Demand Analysis Many Prices Methods

项目摘要

This research develops machine learning methods to estimate economic welfare from big data. Scanner data, as collected in grocery and other retail stores, provides big data that can be used to estimate economic welfare. The investigator develops new double machine learning estimators of economic welfare based on big data. These estimators combine novel machine learning of certain economic weights with machine learning estimators of demand functions to do double machine learning estimation of welfare. These estimators are also generalized and applied to many other problems. In addition, this research uses the fact that scanner data follows individuals over time. Hence, individual demand functions are estimated and averaged to construct improved welfare measures.The objective of this research is to develop and apply economic demand analysis for large data sets that include many prices, such as scanner data. A common feature of scanner data is that cross price effects tend to be small, often an order of magnitude smaller than own price effects. This feature suggests that machine learning methods that allow for approximate sparsity, where most cross price effects are small, might be useful in practice. This research develops double machine learning estimators of exact consumer surplus and other welfare effects. The investigator uses novel machine learning of objects in Riesz representations that are not conditional expectations. This research produces a general method of double machine learning for generalized method of moments with first step series estimators. Scanner data is often panel data, where individuals or households are followed over time. The investigator further derives identification results for demand in panel data with general heterogeneity, and analyzes regularized fixed effect panel data estimators of average effects that can be applied to demand estimation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项研究开发了机器学习方法，以从大数据中估算经济福利。扫描仪数据在杂货店和其他零售商店中收集，提供了可用于估计经济福利的大数据。研究人员根据大数据开发了新的双重机器学习估计器。这些估计器将某些经济重量的新机器学习与需求功能的机器学习估计器相结合，以执行双重机器学习福利估计。这些估计量也被广义化，并应用于许多其他问题。此外，该研究还使用了这样一个事实，即扫描仪数据随着时间的流逝跟随个人。因此，估计和平均个人需求功能以构建改进的福利措施。这项研究的目的是针对包括许多价格（例如扫描仪数据）的大型数据集开发和应用经济需求分析。扫描仪数据的一个共同特征是，交叉价格影响往往很小，通常比自身价格效应小的数量级。该功能表明，允许大多数频率较小的稀疏性的机器学习方法在实践中可能很有用。这项研究开发了确切的消费者盈余和其他福利效应的双机器学习估计器。研究人员在Riesz表示中使用新的机器学习不是有条件的期望。这项研究为具有第一步系列估计器的一般矩方法提供了双重机器学习的通用方法。扫描仪数据通常是面板数据，随着时间的推移，遵循个人或家庭。研究人员进一步得出了具有一般异质性的面板数据需求的识别结果，并分析了可以应用于需求估计的平均效应的正则固定效应面板数据估计值。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来获得支持的。