publ-mit-podpubl-mit-podBantle, Melissa2024-04-082024-04-082024-03-152024https://hohpublica.uni-hohenheim.de/handle/123456789/6944The 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.enghttp://opus.uni-hohenheim.de/doku/lic_mit_pod.phpMachine learningCartel screenFuel retail market330Maschinelles LernenPreispolitikKraftstoffWorkingPaper1883605636urn:nbn:de:bsz:100-opus-23003