Browsing by Author "Visser, Amy Sharon"
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- ItemAssessing cartel detection and damages in simulated markets : a comparative study of econometric and machine learning approaches(Stellenbosch : Stellenbosch University, 2024-03) Visser, Amy Sharon; Boshoff, Willem; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Economics.ENGLISH SUMMARY: Collusion among firms, with the intent to artificially elevate prices, has far-reaching implications for market competition and consumer welfare. This thesis explores the detection of structural breaks in simulated price data under collusion, and their subsequent impact on damage estimation. This contribution is significant in the field of competition economics as it allows for consideration of the implications for econometric methods aimed at identifying and measuring collusive effects in the age of machine learning alternatives. A combination of econometric and machine learning approaches, including Lasso regression, random forest regression and classification, logistic regression, and Bai-Perron structural break testing are rigorously examined against four distinct data generating processes simulated to mimic the behaviours of cartels observed in the market. These include a deterministic switch data generating process, a recurrent switch data generating process, a phased switch data generating process, and a Markov-switching data generating process. The study reveals that the Lasso model consistently outperforms the other methods in estimating structural breaks, demonstrating superior performance in identifying cartel and competitive pricing behaviours across the different linear data generating processes. Conversely, the Bai-Perron test exhibits the poorest performance, particularly in Phase and Markov-switching transitions, highlighting its limitations in capturing nuanced structural changes. Furthermore, damage estimation was performed using dummy variables generated by each of the models. All of the empirical models perform relatively well in capturing damages, with the exception of the Bai-Perron model when applied to the phase and Markov-switching data generating processes, further emphasising its limited utility in detecting nuanced switching mechanisms in pricing behaviour. To enhance the analysis, damage estimation was alternatively conducted by predicting movements in the price variable for the Lasso and random forest models. These modifications revealed slight discrepancies in damage predictions, with the Lasso model overpredicting and the random forest model underpredicting damages. Nevertheless, both models remain highly accurate in capturing the economic impact of structural changes in competitive pricing. This research contributes to the field of competition economics by providing a comprehensive analysis of structural break detection and damage estimation methodologies, ultimately demonstrating the practical advantages of the Lasso regression model when applied to linear pricing models. These findings offer valuable insights for policymakers and analysts seeking to better understand and address changes in competitive market dynamics.