Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/228260
Authors: 
Shy, Oz
Year of Publication: 
2020
Series/Report no.: 
Working Paper No. 2020-8
Abstract: 
The study of consumer payment choice at the point of sale involves a classification of payment methods such as cash, credit cards, debit cards, prepaid cards, paper checks, and electronic payments withdrawn from consumers' bank accounts. I describe alternative methods for studying consumer payment choice using some machine learning techniques applied to consumer diary survey data. I then compare the results to the more traditional logistic regression methods. Machine learning techniques have advantages in generating predictions of payment choice, in visualization of the results, and when applied to high-dimensional data. The logistic regression approach has an advantage in interpreting the probability that a buyer uses a specific payment instrument.
Subjects: 
Studying consumer payment choice
point of sale
statistical learning
machinelearning
studying consumer payment choice
point of sale
statistical learning
machine learning
JEL: 
C19
E42
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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