Demand Analysis with Many Prices: Methods and Application
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.
Supported by the National Science Foundation grant #1757140
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