Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/228260 
Autor:innen: 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
Working Paper No. 2020-8
Verlag: 
Federal Reserve Bank of Atlanta, Atlanta, GA
Zusammenfassung: 
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of payment methods such as cash, credit cards, debit cards, prepaid cards, paper checks, and electronic payments withdrawn from consumers' bank accounts.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.
Schlagwörter: 
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 der Erstveröffentlichung: 
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
453.4 kB





Publikationen in EconStor sind urheberrechtlich geschützt.