Detecting Edgeworth Cycles

TL;DR

This paper develops and tests algorithms, including new spectral and machine learning methods, to detect Edgeworth cycles in gasoline prices, highlighting the importance of method choice for policy implications.

Contribution

It formalizes four existing detection methods and introduces six novel approaches, enhancing accuracy in identifying Edgeworth cycles across different datasets.

Findings

Most methods accurately detect cycles in Australian data.

Few methods effectively identify nuanced cycles in German data.

Method choice influences the inferred relationship between cycles and market power.

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.