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Detecting Edgeworth Cycles

Author

Listed:
  • Timothy Holt
  • Mitsuru Igami
  • Simon Scheidegger

Abstract

We propose algorithms to detect "Edgeworth cycles", 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 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.

Suggested Citation

  • Timothy Holt & Mitsuru Igami & Simon Scheidegger, 2021. "Detecting Edgeworth Cycles," Cahiers de Recherches Economiques du Département d'économie 21.16, Université de Lausanne, Faculté des HEC, Département d’économie.
    • Handle: RePEc:lau:crdeep:21.16
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Edgeworth cycles; Fuel prices; Markups; Nonparametric methods;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices

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