Multiple mortality modeling in Poisson Lee-Carter framework

D'Amato, V., Haberman, S., Piscopo, G., Russolillo, M. & Trapani, L. (2016). Multiple mortality modeling in Poisson Lee-Carter framework. Communications in Statistics - Theory and Methods, 45(6), pp. 1723-1732. doi: 10.1080/03610926.2014.960580

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Abstract

The academic literature in longevity field has recently focused on models for detecting multiple population trends (D'Amato et al., 2012b; Njenga and Sherris, 2011; Russolillo et al., 2011, etc.). In particular, increasing interest has been shown about "related" population dynamics or "parent" populations characterized by similar socioeconomic conditions and eventually also by geographical proximity. These studies suggest dependence across multiple populations and common long-run relationships between countries (for instance, see Lazar et al., 2009). In order to investigate cross-country longevity common trends, we adopt a multiple population approach. The algorithm we propose retains the parametric structure of the Lee-Carter model, extending the basic framework to include some cross-dependence in the error term. As far as time dependence is concerned, we allow for all idiosyncratic components (both in the common stochastic trend and in the error term) to follow a linear process, thus considering a highly flexible specification for the serial dependence structure of our data. We also relax the assumption of normality, which is typical of early studies on mortality (Lee and Carter, 1992) and on factor models (see e.g., the textbook by Anderson, 1984). The empirical results show that the multiple Lee-Carter approach works well in the presence of dependence.

Item Type: Article
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in Communications in Statistics - Theory and Methods on 7 Nov 2015, available online: http://www.tandfonline.com/10.1080/03610926.2014.960580
Uncontrolled Keywords: Factor models, Lee–Carter model, Serial and cross-sectional correlation, Sieve bootstrap, Vector auto-regression
Subjects: H Social Sciences > HG Finance
Divisions: Cass Business School > Faculty of Actuarial Science & Insurance
URI: http://openaccess.city.ac.uk/id/eprint/13966

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