• xmlui.mirage2.page-structure.header.title
    • français
    • English
  • Help
  • Login
  • Language 
    • Français
    • English
View Item 
  •   BIRD Home
  • CEREMADE (UMR CNRS 7534)
  • CEREMADE : Publications
  • View Item
  •   BIRD Home
  • CEREMADE (UMR CNRS 7534)
  • CEREMADE : Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Browse

BIRDResearch centres & CollectionsBy Issue DateAuthorsTitlesTypeThis CollectionBy Issue DateAuthorsTitlesType

My Account

LoginRegister

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors
Thumbnail

Forecasting mortality rate improvements with a high-dimensional VAR

Guibert, Quentin; Lopez, Olivier; Piette, Pierrick (2019), Forecasting mortality rate improvements with a high-dimensional VAR, Insurance. Mathematics and Economics, 88, p. 255-272. 10.1016/j.insmatheco.2019.07.004

View/Open
Manuscript_Mortalite_Enet.pdf (1.038Mb)
Type
Article accepté pour publication ou publié
Date
2019
Journal name
Insurance. Mathematics and Economics
Volume
88
Pages
255-272
Publication identifier
10.1016/j.insmatheco.2019.07.004
Metadata
Show full item record
Author(s)
Guibert, Quentin cc
Laboratoire de Sciences Actuarielle et Financière [SAF]
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Lopez, Olivier cc

Piette, Pierrick cc
Laboratoire de Probabilités, Statistique et Modélisation [LPSM (UMR_8001)]
Abstract (EN)
Forecasting mortality rates is a problem which involves the analysis of high-dimensional time series. Most of usual mortality models propose to decompose the mortality rates into several latent factors to reduce this complexity. These approaches, in particular those using cohort factors, have a good fit, but they are less reliable for forecasting purposes. One of the major challenges is to determine the spatial–temporal dependence structure between mortality rates given a relatively moderate sample size. This paper proposes a large vector autoregressive (VAR) model fitted on the differences in the log-mortality rates, ensuring the existence of long-run relationships between mortality rate improvements. Our contribution is threefold. First, sparsity, when fitting the model, is ensured by using high-dimensional variable selection techniques without imposing arbitrary constraints on the dependence structure. The main interest is that the structure of the model is directly driven by the data, in contrast to the main factor-based mortality forecasting models. Hence, this approach is more versatile and would provide good forecasting performance for any considered population. Additionally, our estimation allows a one-step procedure, as we do not need to estimate hyper-parameters. The variance–covariance matrix of residuals is then estimated through a parametric form. Secondly, our approach can be used to detect nonintuitive age dependence in the data, beyond the cohort and the period effects which are implicitly captured by our model. Third, our approach can be extended to model the several populations in long run perspectives, without raising issue in the estimation process. Finally, in an out-of-sample forecasting study for mortality rates, we obtain rather good performances and more relevant forecasts compared to classical mortality models using the French, US and UK data. We also show that our results enlighten the so-called cohort and period effects for these populations.
Subjects / Keywords
Mortality forecasting; High-dimensional time series; Vector autoregression; Elastic-net; Age-cohort effect
JEL
C18 - Methodological Issues: General
C32 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
C52 - Model Evaluation, Validation, and Selection
C53 - Forecasting and Prediction Methods; Simulation Methods

Related items

Showing items related by title and author.

  • Thumbnail
    Bridging the Li-Carter's gap: a locally coherent mortality forecast approach 
    Guibert, Quentin; Loisel, Stéphane; Lopez, Olivier; Piette, Pierrick (2020) Document de travail / Working paper
  • Thumbnail
    Sampling High-Dimensional Gaussian Distributions for General Linear Inverse Problems 
    Orieux, François; Féron, Olivier; Giovannelli, Jean-François (2012) Article accepté pour publication ou publié
  • Thumbnail
    Rank penalized estimators for high-dimensional matrices 
    Klopp, Olga (2011) Article accepté pour publication ou publié
  • Thumbnail
    The interchange process on high-dimensional products 
    Hermon, Jonathan; Salez, Justin (2021-02) Article accepté pour publication ou publié
  • Thumbnail
    Approches nouvelles des modèles GARCH multivariés en grande dimension 
    Poignard, Benjamin (2017-06-15) Thèse
Dauphine PSL Bibliothèque logo
Place du Maréchal de Lattre de Tassigny 75775 Paris Cedex 16
Phone: 01 44 05 40 94
Contact
Dauphine PSL logoEQUIS logoCreative Commons logo