
This replication package contains the data and the code to generate the main results reported in "Detecting Edgeworth Cycles" by Timothy Holt, Mitsuru Igami, and Simon Scheidegger, to be published in the February 2024 issue of The Journal of Law and Economics. Additionally, this package also allows the users to apply these pre-trained models to new datasets of their choice. As an example of such a new dataset, we include the entire German dataset available at the time of our research (2014:Q4–2020:Q4), including both manually labeled and unlabeled subsamples. Finally, this package includes tools to facilitate the acquisition and pre-processing of the most recent data from Germany, which is updated every day on the Tankerkoenig website (at the time of our preparation of this package). [3/29/2024 update] The URL for the German antitrust authority's fuel-data website has changed to https://www.bundeskartellamt.de/EN/Tasks/markettransparencyunit_fuels/markettransparencyunit_fuels.html.
Antitrust, Edgeworth cycles, Markups, Economics, Deep neural networks, Machine learning, Fuel prices, Nonparametric methods, Industrial Organization, Spectral analysis
Antitrust, Edgeworth cycles, Markups, Economics, Deep neural networks, Machine learning, Fuel prices, Nonparametric methods, Industrial Organization, Spectral analysis
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
