title: Identification and forecasting in mortality models.
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.
authors.affiliation: Department of Economics, University of Oxford, Oxford OX1 2JD, UK ; Programme on Economic Modelling, INET, University of Oxford, Oxford OX1 2JD, UK ; Nuffield College, Oxford OX1 1NF, UK.
authors.forename: Bent
authors.identifier: 0000-0002-1567-4652
authors.initials: B
authors.lastname: Nielsen
authors.affiliation: Cass Business School, City University London, 106 Bunhill Row, London EC1Y 8TZ, UK.
authors.forename: Jens P
authors.initials: JP
authors.lastname: Nielsen
mesh_terms: D000465:Algorithms; D006801:Humans; D015233:Models, Statistical; D009026:Mortality