Detecting Edgeworth Cycles

44 Pages Posted: 5 Oct 2021 Last revised: 22 Nov 2021

See all articles by Timothy Holt

Timothy Holt

Università della Svizzera italiana

Mitsuru Igami

Yale University - Department of Economics ; Yale University - Cowles Foundation

Simon Scheidegger

University of Lausanne - School of Economics and Business Administration (HEC-Lausanne)

Date Written: November 18, 2021

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

JEL Classification: C45, C55, L13, L41

Suggested Citation

Holt, Timothy and Igami, Mitsuru and Scheidegger, Simon, Detecting Edgeworth Cycles (November 18, 2021). Available at SSRN: https://ssrn.com/abstract=3934367 or http://dx.doi.org/10.2139/ssrn.3934367

Timothy Holt

Università della Svizzera italiana

Lugano
Switzerland

Mitsuru Igami (Contact Author)

Yale University - Department of Economics ( email )

28 Hillhouse Ave
New Haven, CT 06520-8268
United States

Yale University - Cowles Foundation

Box 208281
New Haven, CT 06520-8281
United States

Simon Scheidegger

University of Lausanne - School of Economics and Business Administration (HEC-Lausanne) ( email )

Unil Dorigny, Batiment Internef
Lausanne, 1015
Switzerland

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