Calendar effect and in-sample forecasting

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

“…Recently, [35,36] defined the term "in-sample forecasting" to mean forecasting a structured function in regions where the function is not observed, but where it is determined by its values in the observed region. This has several advantages over methods based on time series analysis, as [37] discussed. Standard APC models can be understood as discrete density models with a simple multiplicative structure (or log lineal).…”

“…The aim of our paper is to propose, apply and further illustrate in-sample forecasting, by providing a projection of future mesothelioma deaths using simple methodology. We build on the approach of [37] by using the updated dataset from the HSE to conduct a study that forecasts the number of deaths due to mesothelioma. Furthermore, we analyse the differences in forecasts of future expected mesothelioma deaths due in respect of three models: the model applied in this paper, the discrete APC model of [19] and the model using synthetic exposure measures adopted by HSE [39].…”

“…Our paper makes two main contributions: First, we illustrate the method in which the approach of [37] addresses the problem of the lack of exposure data by applying the method to a real dataset that would normally pose a challenge to an insurer's claim reserving methodology. Our study expands on the method in terms of usability and applications.…”

“…Our study expands on the method in terms of usability and applications. While [37] developed an extended continuous chain ladder model which allows for a calendar (period) effect and they illustrated the broad applicability using two empirical examples, we are able to explore further implications of the method when applying it to the challenging problem of mesothelioma mortality forecasting. We show that the approach is very flexible by introducing a smoothing technique for the temporal effects.…”

“…The remainder of the paper is organised as follows: In Section 2 we formulate the approach of [37] in the context of mortality forecasting and show how it can be applied to provide forecasts of the number of deaths due to mesothelioma. We conduct this in three parts: first, a statistical structured density model is motivated and formulated; second, the density components are estimated non-parametrically; and finally, mortality projections are made into the future.…”