Calendar Effect and In-Sample Forecasting Applied to Mesothelioma Mortality Data

Alex Isakson, Simone Krummaker, María Dolores Martínez-Miranda and Ben Rickayzen
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Alex Isakson: Bayes Business School (Formerly Cass), City, University of London, London EC1Y 8TZ, UK
Simone Krummaker: Bayes Business School (Formerly Cass), City, University of London, London EC1Y 8TZ, UK
María Dolores Martínez-Miranda: Department of Statistics and Operations Research, University of Granada, 18071 Granada, Spain
Ben Rickayzen: Bayes Business School (Formerly Cass), City, University of London, London EC1Y 8TZ, UK

Mathematics, 2021, vol. 9, issue 18, 1-17

Abstract: In this paper, we apply and further illustrate a recently developed extended continuous chain ladder model to forecast mesothelioma deaths. Making such a forecast has always been a challenge for insurance companies as exposure is difficult or impossible to measure, and the latency of the disease usually lasts several decades. While we compare three approaches to this problem, we show that the extended continuous chain ladder model is a promising benchmark candidate for asbestosis mortality forecasting due to its flexible and simple forecasting strategy. Furthermore, we demonstrate how the model can be used to provide an update for the forecast of the number of deaths due to mesothelioma in Great Britain using in recent Health and Safety Executive (HSE) data.

Keywords: continuous chain ladder; age-period-cohort model; backfitting; density estimation; kernel smoothing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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