P. Barrieu, H. Bensusan, N. Karoui, C. Hillairet, S. Loisel et al., Understanding, modelling and managing longevity risk: key issues and main challenges, Scandinavian Actuarial Journal, vol.8, issue.2, 2012.
DOI : 10.1007/s00285-008-0202-2

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.394.6812

S. Basu and G. Michailidis, Regularized estimation in sparse high-dimensional time series models. The Annals of Statistics 43, p.15351567, 2015.

H. Bensusan, A. Boumezoued, N. Karoui, and S. Loisel, Bridging the gap from microsimulation practice to population models: a survey, 2010.

P. J. Bickel and E. Levina, Covariance regularization by thresholding. The Annals of Statistics, p.25772604, 2008.

J. Bien and R. J. Tibshirani, Sparse estimation of a covariance matrix, Biometrika, vol.98, issue.4, p.807820, 2011.
DOI : 10.1093/biomet/asr054

C. Bohk-ewald and R. Rau, Probabilistic mortality forecasting with varying age-specic survival improvements, pp.41118-41134, 2017.

H. Booth and L. Tickle, Mortality Modelling and Forecasting: a Review of Methods, Annals of Actuarial Science, vol.1, issue.22, 2008.
DOI : 10.1017/S1357321700001100

H. Booth, J. Maindonald, and L. Smith, Applying Lee-Carter under conditions of variable mortality decline, Population Studies, vol.291, issue.3, p.325336, 2002.
DOI : 10.2307/2530689

A. Boumezoued, Approches micro-macro des dynamiques de populations hétérogènes structurées par âge Application aux processus auto-excitants et à la démographie Improving HMD mortality estimates with HFD fertility data, 2016.

M. Börger, D. Fleischer, and N. Kuksin, Abstract, ASTIN Bulletin, vol.49, issue.01, 2014.
DOI : 10.1007/s11857-010-0125-z

N. Brouhns, M. Denuit, and J. K. Vermunt, A Poisson log-bilinear regression approach to the construction of projected lifetables, Insurance: Mathematics and Economics, vol.31, issue.3, pp.373-393, 2002.
DOI : 10.1016/S0167-6687(02)00185-3

A. J. Cairns, D. Blake, and K. Dowd, A Two-Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration Modelling and management of mortality risk: a review, Journal of Risk and Insurance Scandinavian Actuarial Journal, vol.734, pp.2-3, 1080.

A. J. Cairns, M. Kallestrup-lamb, C. P. Rosenskjold, D. Blake, and K. Dowd, Modelling Socio-Economic Dierences in the Mortality of Danish Males Using a New Auence Index, 2016.

A. J. Cairns, D. Blake, K. Dowd, and A. R. Kessler, Phantoms never die: living with unreliable population data, Statistics in Society) 179.4, p.9751005, 2016.

A. J. Cairns, D. Blake, K. Dowd, G. D. Coughlan, D. Epstein et al., A quantitative comparison of stochastic mortality models using data from England and Wales and the United States, North American Actuarial Journal, vol.131, p.135, 2009.

A. J. Cairns, D. Blake, K. Dowd, G. D. Coughlan, and M. Khalaf-allah, Bayesian Stochastic Mortality Modelling for Two Populations. ASTIN Bulletin: The Journal of the International Actuarial Association 41, 2011.

C. M. Chai, T. K. Siu, and X. Zhou, A double-exponential GARCH model for stochastic mortality, European Actuarial Journal 3.2. 00003, pp.385406-385416, 2013.
DOI : 10.1017/S1357321700002762

A. Chatterjee and S. N. Lahiri, Bootstrapping Lasso Estimators, Journal of the American Statistical Association, vol.106, issue.494, p.608625, 2011.
DOI : 10.1198/jasa.2011.tm10159

H. Chen, R. Macminn, and T. Sun, Multi-population mortality models: A factor copula approach Special Issue: Longevity Nine -the Ninth International Longevity Risk and Capital Markets Solutions Conference 63, Insurance: Mathematics and Economics, 2015.

M. C. Christiansen, E. Spodarev, and V. Unseld, Dierences in European Mortality Rates: A Geometric Approach on the AgePeriod Plane. ASTIN Bulletin: The Journal of the International Actuarial Association 45, 2015.

P. Doukhan, D. Pommeret, J. Rynkiewicz, and Y. Salhi, A class of random eld memory models for mortality forecasting, Insurance: Mathematics and Economics, 2017.

K. Dowd, A. J. Cairns, D. Blake, G. D. Coughlan, and M. Khalaf-allah, A Gravity Model of Mortality Rates for Two Related Populations, North American Actuarial Journal, vol.14, issue.5570, 2011.
DOI : 10.1093/ije/14.1.124

V. Enchev, T. Kleinow, and A. J. Cairns, Multi-population mortality models: fitting, forecasting and comparisons, Scandinavian Actuarial Journal, vol.41, issue.4, 2016.
DOI : 10.1056/NEJMsr043743

J. Fan, J. Lv, and L. Qi, Sparse High Dimensional Models in Economics. Annual review of economics 3, 2011.

J. Friedman, T. Hastie, and R. Tibshirani, Regularization Paths for Generalized Linear Models via Coordinate Descent, Journal of Statistical Software, vol.33, issue.1, p.122, 2010.
DOI : 10.18637/jss.v033.i01

Y. Furman, VAR Estimation with the Adaptive Elastic Net. SSRN Scholarly Paper ID 2456510, Social Science Research Network, 2014.

D. Gefang, Bayesian doubly adaptive elastic-net Lasso for VAR shrinkage, International Journal of Forecasting, vol.30, issue.1, p.111, 2014.
DOI : 10.1016/j.ijforecast.2013.04.004

C. W. Granger, Investigating Causal Relations by Econometric Models and Cross-spectral Methods, Econometrica, vol.373, pp.424438-424448, 1969.

S. Haberman and A. Renshaw, Parametric mortality improvement rate modelling and projecting, Insurance: Mathematics and Economics, vol.50, issue.3, 2012.
DOI : 10.1016/j.insmatheco.2011.11.005

L. J. Hahn, A Bayesian Multi-Population Mortality Projection Model, 2014.

A. E. Hoerl and R. W. Kennard, Ridge regression: Biased estimation for nonorthogonal problems, p.5567, 1970.

A. Hunt and D. Blake, A General Procedure for Constructing Mortality Models, North American Actuarial Journal, vol.18, 2014.

A. Hunt and A. M. Villegas, Robustness and convergence in the LeeCarter model with cohort eects, Insurance: Mathematics and Economics, vol.64, p.186202, 2015.

M. M. Huynen, P. Martens, D. Schram, M. P. Weijenberg, and A. E. Kunst, The Impact of Heat Waves and Cold Spells on Mortality Rates in the Dutch Population, Environmental Health Perspectives, vol.109, issue.5, p.463470, 2001.
DOI : 10.1289/ehp.01109463

E. Izraelewicz, L'eet moisson -l'impact des catastrophes vie sur la mortalité à long terme -Exemple de la canicule de l'été, Bulletin Français, p.113159, 2003.

S. F. Jarner and E. M. Kryger, Modelling Adult Mortality in Small Populations: The Saint Model. ASTIN Bulletin: The Journal of the International Actuarial Association 41, 2011.

R. D. Lee and L. R. Carter, Modeling and Forecasting U. S. Mortality, Journal of the American Statistical Association, vol.87419, 1992.

H. Li and Y. Lu, Coherent Forecasting of Mortality Rates: A Sparse Vector-Autoregression Approach. ASTIN Bulletin: The Journal of the IAA 47, 2017.

N. Li and R. Lee, Coherent Mortality Forecasts for a Group of Populations: An Extension of the Lee-Carter Method, Demography, vol.42, issue.3, p.575594, 2005.
DOI : 10.1353/dem.2005.0021

N. Li, R. Lee, G. , and P. , Extending the Lee-Carter method to model the rotation of age patterns of mortality-decline for long-term projection. Demography 50, pp.20372051-20372061, 2013.

R. Opgen-rhein and K. Strimmer, From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data, BMC Systems Biology, vol.1, issue.1, p.37, 2007.
DOI : 10.1186/1752-0509-1-37

P. Perron, Trends and random walks in macroeconomic time series, Journal of Economic Dynamics and Control, vol.12, issue.2-3, p.297332, 1988.
DOI : 10.1016/0165-1889(88)90043-7

R. Plat, On stochastic mortality modeling, Insurance: Mathematics and Economics, vol.453, p.393404, 2009.

R. Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2017.

A. E. Renshaw and S. Haberman, A cohort-based extension to the LeeCarter model for mortality reduction factors On simulation-based approaches to risk measurement in mortality with specic reference to Poisson LeeCarter modelling, Insurance: Mathematics and Economics Insurance: Mathematics and Economics, vol.383, issue.422, 2006.

S. E. Said and D. A. Dickey, Testing for unit roots in autoregressive-moving average models of unknown order, Biometrika, vol.71, issue.3, p.599607, 1984.
DOI : 10.1093/biomet/71.3.599

Y. Salhi and S. Loisel, Basis risk modelling: a cointegration-based approach, Statistics, vol.99, issue.11, 2017.
DOI : 10.1016/j.insmatheco.2005.06.008

URL : https://hal.archives-ouvertes.fr/hal-00746859

J. Schäfer and K. Strimmer, A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics, Statistical Applications in Genetics and Molecular Biology, vol.18, issue.1, 2005.
DOI : 10.1093/bioinformatics/18.2.287

S. Song and P. J. Bickel, Large vector auto regressions, 2011.

E. Spodarev, E. Shmileva, R. , and S. , Extrapolation of stationary random elds. arXiv preprint, 2013.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B, p.267288, 1996.

L. Toulemon and M. Barbieri, The mortality heat wave in France: Investigating thèharvesting' eect and other long-term consequences, Population Studies, vol.62, 2003.

S. Vazzoler, L. Frattarolo, and M. Billio, sparsevar: A Package for Sparse VAR/VECM Estimation, 2016.

A. M. Villegas, V. Kaishev, and P. Millossovich, StMoMo: An R Package for Stochastic Mortality Modelling, SSRN Electronic Journal, 2017.
DOI : 10.2139/ssrn.2698729

I. Wilms and C. Croux, Forecasting using sparse cointegration, International Journal of Forecasting, vol.32, issue.4, p.12561267, 2016.
DOI : 10.1016/j.ijforecast.2016.04.005

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.67, issue.2, p.301320, 2005.