Time-Simultaneous Fan Charts: Applications to stochastic life table forecasting
Notice bibliographique
Résumé
When it comes to product pricing and reserving, actuaries often need life tables that include a forecast of future longevity improvement.However, the production of such tables is not straightforward, because the demographic future of any human population is a result of complex and only partially understood mechanisms, and is highly uncertain.In recent years, actuaries have been understandably concerned about error in the mortality assumptions they make.Part of their response is a new wave of work that is focused on the forecasting of uncertainty in longevity improvement, rather than producing a single mortality projection that will almost surely be wrong.This goal is accomplished by using stochastic mortality models, which have uncertainty embedded within them, as reflected in historical changes.Given a fitted stochastic mortality model, we can express the uncertainty associated with future death rates in terms of confidence or prediction intervals.Recently, a group of researchers has proposed using fan charts to display prediction intervals for future mortality rates.These charts are highly parallel to the wellknown inflation fan charts, which have been produced periodically by the Bank of England since 1996.A fan chart depicts prediction intervals at different levels of confidence simultaneously.In particular, it shows the central 10% prediction interval with the heaviest shading, surrounded by the 20%, 30%, ..., 90% prediction intervals with progressively lighter shading.We can therefore interpret the degree of shading as the likelihood of the outcome -the darker the shading, the more likely the outcome.Mortality fan charts are highly useful to actuaries, because they provide guidance on how to determine appropriate margins for adverse deviations.Existing mortality fan charts are based on isolated pointwise prediction intervals.By pointwise we mean that the interval reflects uncertainty in a quantity at a single point of time, but it does not account for any dynamic property of the time-series.However, in actuarial practice, rather than a single death rate at a particular time point, what practitioners need is the entire trajectory of mortality rates for the birth cohort in question.Specifically, of their interest would be questions like "Within what bounds would the trajectory of cohort mortality rates likely to remain with a certain degree of confidence?" From a statistical viewpoint, a band of pointwise intervals might lead to invalid inference concerning the time trajectory.In particular, unless all trajectories develop very orderly, a band of pointwise confidence intervals would understate the actual uncertainty associated with a random mortality trajectory.In this paper, we overcome this limitation by introducing the concept of time-simultaneous fan charts.In more detail, instead of pointwise intervals, a time-simultaneous fan chart is derived from a prediction band with a prescribed probability of covering the whole time trajectory.We present two numerical methods for producing time-simultaneous fan charts.These methods can be applied to common stochastic mortality models, including the generalized Cairns-Blake-Dowd model.We illustrate the method with mortality data from the populations of Australia and New Zealand.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».