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The Scientific World Journal
Volume 2014, Article ID 347043, 24 pages
http://dx.doi.org/10.1155/2014/347043
Research Article

Identification and Forecasting in Mortality Models

1Department of Economics, University of Oxford, Oxford OX1 2JD, UK
2Programme on Economic Modelling, INET, University of Oxford, Oxford OX1 2JD, UK
3Nuffield College, Oxford OX1 1NF, UK
4Cass Business School, City University London, 106 Bunhill Row, London EC1Y 8TZ, UK

Received 22 January 2014; Accepted 17 April 2014; Published 2 June 2014

Academic Editor: Montserrat Guillén

Copyright © 2014 Bent Nielsen and Jens P. Nielsen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Mortality models often have inbuilt identification issues challenging the statistician. The statistician can choose to work with well-defined freely varying parameters, derived as maximal invariants in this paper, or with ad hoc identified parameters which at first glance seem more intuitive, but which can introduce a number of unnecessary challenges. In this paper we describe the methodological advantages from using the maximal invariant parameterisation and we go through the extra methodological challenges a statistician has to deal with when insisting on working with ad hoc identifications. These challenges are broadly similar in frequentist and in Bayesian setups. We also go through a number of examples from the literature where ad hoc identifications have been preferred in the statistical analyses.