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| SWD-Schlagwörter: |
| Maschinelles Lernen , Preispolitik , Kraftstoff |
| Freie Schlagwörter (Englisch): |
| Machine Learning, Cartel Screens, Fuel Retail Market |
| Institut: |
| Institut für Volkswirtschaftslehre |
| DDC-Sachgruppe: |
| Wirtschaft |
| Dokumentart: |
| ResearchPaper |
| Schriftenreihe: |
| Hohenheim discussion papers in business, economics and social sciences |
| Bandnummer: |
| 2024,01 |
| Sprache: |
| Englisch |
| Erstellungsjahr: |
| 2024 |
| Publikationsdatum: |
| 15.03.2024 |
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Veröffentlichungsvertrag mit der Universitätsbibliothek Hohenheim
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| Kurzfassung auf Englisch: |
| The paper uses a machine learning technique to build up a screen for collusive
behavior. Such tools can be applied by competition authorities but
also by companies to screen the behavior of their suppliers. The method is
applied to the German retail gasoline market to detect anomalous behavior
in the price setting of the filling stations. Therefore, the algorithm identifies
anomalies in the data-generating process. The results show that various
anomalies can be detected with this method. These anomalies in the price
setting behavior are then discussed with respect to their implications for the
competitiveness of the market. |
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