Detecting Edgeworth Cycles

Abstract

We develop and test algorithms to detect "Edgeworth cycles," which are asymmetric price movements that have caused antitrust concerns in many countries. We formalize four existing methods and propose six new methods based on spectral analysis and machine learning. We evaluate their accuracy in station-level gasoline-price data from Western Australia, New South Wales, and Germany. Most methods achieve high accuracy in the first two, but only a few can detect the nuanced cycles in the third. Results suggest whether researchers find a positive or negative statistical relationship between cycles and markups, and hence their implications for competition policy, crucially depends on the choice of methods. We conclude with a set of practical recommendations.

Keywords: Deep neural networks, Edgeworth cycles, Fuel prices, Machine learning, Markups, Nonparametric methods, Spectral analysis

JEL Classification: C45, C55, L13, L41