Detecting Edgeworth Cycles

Timothy Holt, Mitsuru Igami and Simon Scheidegger

Cahiers de Recherches Economiques du Département d'économie from Université de Lausanne, Faculté des HEC, Département d’économie

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.

Keywords: Edgeworth cycles; Fuel prices; Markups; Nonparametric methods (search for similar items in EconPapers)
JEL-codes: C45 C55 L13 L41 (search for similar items in EconPapers)
Pages: 44 pp.
Date: 2021-11
New Economics Papers: this item is included in nep-big, nep-com and nep-ene
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http://www.unil.ch/de/files/live/sites/de/files/working-papers/21.16.pdf (application/pdf)

Related works:
Journal Article: Detecting Edgeworth Cycles (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:lau:crdeep:21.16

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