Detecting Edgeworth Cycles
开发并测试了检测埃奇沃斯周期的算法，发现方法选择会影响周期与加价之间统计关系的结论，为竞争政策分析提供实用建议。
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 with data from Western Australia and New South Wales, but only a few can detect the nuanced cycles in Germany. Results suggest that 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.