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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，以从大数据中估计经济福利。在杂货店和其他零售店收集的扫描仪数据提供了可用于估计经济福利的大数据。研究人员开发了基于大数据的经济福利新的双机器学习估计器。这些估计器将某些经济权重的新机器学习与需求函数的机器学习估计器相结合，以进行福利的双重机器学习估计。这些估计也被推广并应用于许多其他问题。此外，这项研究还利用了扫描仪数据随时间推移而跟踪个人的事实。因此，个人需求函数的估计和平均构建改进的福利measures.The本研究的目的是开发和应用的经济需求分析，包括许多价格，如扫描仪数据的大数据集。扫描数据的一个共同特征是交叉价格效应往往很小，通常比自身价格效应小一个数量级。这一特征表明，允许近似稀疏性的机器学习方法，其中大多数交叉价格效应都很小，在实践中可能是有用的。本研究开发了精确消费者剩余和其他福利效应的双机器学习估计器。研究人员使用Riesz表示中的对象进行新的机器学习，这些对象不是条件期望。本文提出了一种基于第一步序列估计的广义矩量法的双机器学习的一般方法。扫描数据通常是面板数据，其中个人或家庭随时间推移而被跟踪。研究者进一步推导出具有一般异质性的面板数据中需求的识别结果，并分析可应用于需求估计的平均效应的正则化固定效应面板数据估计值。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。