Abstract

A very popular forecasting tool in the actuarial sciences is the so-called chain ladder. Mammen et al. (2015) recently introduced in-sample forecasting - generalizing continuous chain ladder of Mart´ınez-Miranda et al. (2013) - as a general forecasting technique applicable in many fields. The main aim of this paper is to develop an extended version of the continuous chain ladder which is of interest not only for actuaries but which has many potential applications in economics and other fields. The statistical problem underlying the extended continuous chain ladder is to estimate and forecast a structured nonparametric density. In the theoretical part of the paper, we develop methodology to approach this problem. The usefulness of the methods is illustrated by empirical examples from economics and the actuarial sciences.

Publication Type:	Article
Additional Information:	© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords:	nonparametric density estimation, kernel smoothing, backfitting
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HF Commerce > HF5601 Accounting Q Science > QA Mathematics
Departments:	Business School > Actuarial Science & Insurance
Date Deposited:	21 Oct 2020 10:23
URI:	https://openaccess.city.ac.uk/id/eprint/25130

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