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EnglishThe academic literature in longevity field has recently focused on models for detecting multiple population trends ([9],[17],[20], etc.). In particular increasing interest has been shown about “related” population dynamics or “parent” populations characterized by similar socio-economic conditions and eventually also by geographical proximity. These studies suggest dependence across multiple populations and common long run relationships between countries (for instance see [13]). 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 [14] and on factor models (see e.g. the textbook by [1]). The empirical results show that the Multiple Lee Carter Approach works well in presence of dependence.
| Titolo: | Multiple Population Projections by Lee Carter Models |
| Autori: | |
| Data di pubblicazione: | 2013 |
| Abstract: | The academic literature in longevity field has recently focused on models for detecting multiple population trends ([9],[17],[20], etc.). In particular increasing interest has been shown about “related” population dynamics or “parent” populations characterized by similar socio-economic conditions and eventually also by geographical proximity. These studies suggest dependence across multiple populations and common long run relationships between countries (for instance see [13]). 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 [14] and on factor models (see e.g. the textbook by [1]). The empirical results show that the Multiple Lee Carter Approach works well in presence of dependence. |
| Handle: | http://hdl.handle.net/11386/4413856 |
| ISBN: | 9786188069824 |
| Appare nelle tipologie: | 4.1.2 Proceedings con ISBN |
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