Short Bio

headshot of the author
I'm a quantitative economist with a strong background in econometrics, statistics, data science, and machine learning. My academic research focuses on time series analysis, with a particular interest in model selection for non-stationary data. During my time as an Associate Researcher/PhD student at the Chair of Econometrics at UDE, I developed a diverse skill set that bridges theoretical knowledge and practical application.

I enjoy crafting reliable, code-driven solutions to analytical challenges and uncovering insights in data-rich environments. Sharing knowledge with interdisciplinary teams is equally rewarding, particularly using modern reproducibility tools.

Perhaps we've crossed paths on StackOverflow? As an RStudio/Posit Certified R Trainer, I'm happy to offer tailored advice—don't hesitate to reach out!

I co-authored some OER resources:

Publications and Working Papers

C Hanck, M Arnold, A Gerber, M Schmelzer (2019). Introduction to Econometrics with R. Essen: University of Duisburg-Essen.
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C Hanck, MC Arnold (2022). Hierarchical Bayes modelling of penalty conversion rates of Bundesliga players. AStA Advances in Statistical Analysis, 1-28
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MC Arnold, T Reinschlüssel (2024). Adaptive Unit Root Inference in Autoregressions using the Lasso Solution Path. arXiv preprint arXiv:2404.06205
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MC Arnold, C Hanck (2019). On combining evidence from heteroskedasticity robust panel unit root tests in pooled regressions. Journal of Risk and Financial Management 12 (3), 117
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MC Arnold, T Reinschlüssel (2024). Bootstrap Adaptive Lasso Solution Path Unit Root Tests. arXiv preprint arXiv:2409.07859
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T Reinschlüssel, MC Arnold (2024). Information-Enriched Selection of Stationary and Non-Stationary Autoregressions using the Adaptive Lasso. arXiv preprint arXiv:2402.16580
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MC Arnold (2024). Information-Enriched Selection of Stationary and Non-Stationary Autoregressions using the Adaptive Lasso. arXiv. org
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