An Empirical Comparison of Static Count Panel Data Models: the Case of Vehicle Fires in Stockholm County

• Main Authors: Pihl, Svante, Olivetti, Leonardo
• Format: Others
• Language: English
• Published: Uppsala universitet, Statistiska institutionen 2020
• Subjects: Low-Mean Count Data; Poisson Regression; Negative-Binomial Regression; Panel Data Models; Vehicle Fires; Distributional Assumptions; Probability Theory and Statistics; Sannolikhetsteori och statistik
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412014

In this paper we study the occurrences of outdoor vehicle fires recorded by the Swedish Civil Contingencies Agency (MSB) for the period 1998-2019, and build static panel data models to predict future occurrences of fire in Stockholm County. Through comparing the performance of different models, we look at the effect of different distributional assumptions for the dependent variable on predictive performance. Our study concludes that treating the dependent variable as continuous does not hamper performance, with the exception of models meant to predict more uncommon occurrences of fire. Furthermore, we find that assuming that the dependent variable follows a Negative Binomial Distribution, rather than a Poisson Distribution, does not lead to substantial gains in performance, even in cases of overdispersion. Finally, we notice a slight increase in the number of vehicle fires shown in the data, and reflect on whether this could be related to the increased population size.