arXiv subjects

Daniel Winkler

Publications and source records attributed to Daniel Winkler.

3 recordsLinked to original sources

arXiv2024

Does TikTok Promote or Cannibalize Music Streaming? Estimands and Identification with Heavy-Tailed Outcomes

We study how TikTok affects demand for music on paid streaming platforms. We use Universal Music Group's (UMG) global withdrawal of its catalog from TikTok as a quasi-natural experiment. Recent work using this setting reaches mixed conclusions about whether TikTok promotes or cannibalizes streaming demand. We show that these findings can be reconciled by making the estimand explicit: with heavy-tailed exposure and outcomes, common difference-in-differences (DiD) implementations in levels, logs, and Poisson answer different economic questions. In our data, the top 10% of songs account for 96% of TikTok creations and 76% of Spotify streams, which makes the distinction between the typical song and the economically consequential song central. We find that removing TikTok access lowers Spotify demand for UMG titles, with losses concentrated among viral songs and little economically meaningful change for the long tail. Because the viral head accounts for a disproportionate share of listening and revenue, these losses drive aggregate implications. A TikTok creator-side analysis shows that some activity reallocates toward non-UMG audio when UMG content is unavailable. This substitution is limited in magnitude but economically relevant for interpreting the treatment effect because streaming compensation depends on relative stream shares. Finally, using the 2025 U.S. TikTok outage, which affected all labels symmetrically and is not subject to the label-specific spillover concern as the UMG withdrawal, we find corroborating evidence that disruptions to TikTok access reduce monetized streaming. We also provide a practitioner companion that guides the choice of DiD estimands, estimators, and diagnostics in heavy-tailed outcome settings.

econ.GN↗

arXiv2022

Flexible yet Sparse Bayesian Survival Models with Time-Varying Coefficients and Unobserved Heterogeneity

Survival analysis is an important area of medical research, yet existing models often struggle to balance simplicity with flexibility. Simple models require minimal adjustments but come with strong assumptions, while more flexible models require significant input and tuning from researchers. We present a survival model using a Bayesian hierarchical shrinkage method that automatically determines whether each covariate should be treated as static, time-varying, or excluded altogether. This approach strikes a balance between simplicity and flexibility, minimizes the need for tuning, and naturally quantifies uncertainty. The method is supported by an efficient Markov chain Monte Carlo sampler, implemented in the R package shrinkDSM. Comprehensive simulation studies and an application to a clinical dataset involving patients with adenocarcinoma of the gastroesophageal junction showcase the advantages of our approach compared to existing models.

stat.ME↗

arXiv2018

Predicting Natural Hazards with Neuronal Networks

Gravitational mass flows, such as avalanches, debris flows and rockfalls are common events in alpine regions with high impact on transport routes. Within the last few decades, hazard zone maps have been developed to systematically approach this threat. These maps mark vulnerable zones in habitable areas to allow effective planning of hazard mitigation measures and development of settlements. Hazard zone maps have shown to be an effective tool to reduce fatalities during extreme events. They are created in a complex process, based on experience, empirical models, physical simulations and historical data. The generation of such maps is therefore expensive and limited to crucially important regions, e.g. permanently inhabited areas. In this work we interpret the task of hazard zone mapping as a classification problem. Every point in a specific area has to be classified according to its vulnerability. On a regional scale this leads to a segmentation problem, where the total area has to be divided in the respective hazard zones. The recent developments in artificial intelligence, namely convolutional neuronal networks, have led to major improvement in a very similar task, image classification and semantic segmentation, i.e. computer vision. We use a convolutional neuronal network to identify terrain formations with the potential for catastrophic snow avalanches and label points in their reach as vulnerable. Repeating this procedure for all points allows us to generate an artificial hazard zone map. We demonstrate that the approach is feasible and promising based on the hazard zone map of the Tirolean Oberland. However, more training data and further improvement of the method is required before such techniques can be applied reliably.

eess.IV↗