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321.
Professional team sports provide an ideal real world experiment to explore the effect of top executive replacement due to
the availability of excellent performance data. This paper investigates how replacing the coach affects the performance of
football teams. Analyzing almost 2,000 matches of the Austrian premier football league by ordinal (probit) regression techniques
we find that a new coach tends to enhance team performance in home matches but to weaken it in away matches. However, neither
of these effects turns out to be statistically significant. Nevertheless, the much discussed coach effect—ousting an underperforming
coach in order to improve team performance at least temporarily—may make sense despite the costs involved by providing stronger
ex-ante incentives for effort.
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Franz WirlEmail: |
322.
323.
Felix Lorenz Jonas Willwersch Marcelo Cajias Franz Fuerst 《Real Estate Economics》2023,51(5):1178-1208
Machine Learning (ML) excels at most predictive tasks but its complex nonparametric structure renders it less useful for inference and out-of sample predictions. This article aims to elucidate and enhance the analytical capabilities of ML in real estate through Interpretable ML (IML). Specifically, we compare a hedonic ML approach to a set of model-agnostic interpretation methods. Our results suggest that IML methods permit a peek into the black box of algorithmic decision making by showing the web of associative relationships between variables in greater resolution. In our empirical applications, we confirm that size and age are the most important rent drivers. Further analysis reveals that certain bundles of hedonic characteristics, such as large apartments in historic buildings with balconies located in affluent neighborhoods, attract higher rents than adding up the contributions of each hedonic characteristic. Building age is shown to exhibit a U-shaped pattern in that both the youngest and oldest buildings attract the highest rents. Besides revealing valuable distance decay functions for spatial variables, IML methods are also able to visualise how the strength and interactions of hedonic characteristics change over time, which investors could use to determine the types of assets that perform best at any given stage of the real estate investment cycle. 相似文献