Risk analysis in motor insurance aims to identify factors that increase the frequency of accidents. Telematics data is used to measure behavioural information of drivers. Contextual variables include temperature, rain, wind and traffic conditions that are external to the driver, but may also influence the probability of having an accident, as well as vehicle and personal characteristics. This paper uses a monthly panel data structure and the Poisson model to predict the expected frequency of claims over time. Some meteorological information is included. Two types of claims are considered separately: only those related to at-fault third-party liability accidents, and all types of claims including assistance on the road. A sample of drivers in Spain in 2018–2019 is analysed with information on claiming frequency per month. Drivers were observed for seven months. Our analysis is novel because monthly summaries of telematics information are combined with weather data in a panel structure, revealing that external factors affect the expected claims frequencies. Reckless speeding behaviours and intense urban circulation increase the risk of an accident, which also increases with windy conditions.

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We have analyzed the distribution of residual velocities of nearby stars (within 200 pc of the Sun) looking for space variations on the velocity ellipsoid. We used a sample of 1071 main sequence stars of spectral types B, A and F selected from the Hipparcos Input Catalogue [7] with uvbyHβ photometric data. Ages have been calculated following [1]. Six subsamples with 8.07 ≤ log(age) ≤ 9.45 have been considered.