Alternative Methods for Studying Consumer Payment Choice
Working Paper 2020-8
Abstract: Using machine learning techniques applied to consumer diary survey data, the author of this working paper examines methods for studying consumer payment choice. These techniques, especially when paired with regression analyses, provide useful information for understanding and predicting the payment choices consumers make.
JEL classification: C19, E42
Key words: studying consumer payment choice, point of sale, statistical learning, machine learning
The author thanks participants at the Bank of Canada Retail Payments Workshop held in Ottawa October 24–26, 2018, for valuable comments on an earlier draft. The views expressed here are those of the author and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the author's responsibility.
Please address questions regarding content to Oz Shy, Research Department, Federal Reserve Bank of Atlanta, 1000 Peachtree St. NE, Atlanta, GA 30309.
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