Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/232432 
Year of Publication: 
2021
Series/Report no.: 
CESifo Working Paper No. 8835
Publisher: 
Center for Economic Studies and Ifo Institute (CESifo), Munich
Abstract: 
In this article, we combine machine learning techniques with statistical moments of the gasoline price distribution. By doing so, we aim to detect and predict cartels in the Brazilian retail market. In addition to the traditional variance screen, we evaluate how the standard deviation, coefficient of variation, skewness, and kurtosis can be useful features in identifying anti-competitive market behavior. We complement our discussion with the so-called confusion matrix and discuss the trade-offs related to false-positive and false-negative predictions. Our results show that in some cases, false-negative outcomes critically increase when the main objective is to minimize false-positive predictions. We offer a discussion regarding the pros and cons of our approach for antitrust authorities aiming at detecting and avoiding gasoline cartels.
Subjects: 
cartel screens
price dynamics
fuel retail market
machine learning
JEL: 
C21
C45
C52
K40
L40
L41
Document Type: 
Working Paper
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