Modeling and Forecasting Mortality Rates

2011 ◽  
Author(s):  
Daniel Mitchell ◽  
Patrick L. Brockett ◽  
Rafael Mendoza-Arriaga ◽  
Kumar Muthuraman
Keyword(s):  
Mortality Rates ◽  
Modeling And Forecasting

Modeling and forecasting duration-dependent mortality rates

2015 ◽  
Vol 83 ◽  
pp. 65-81 ◽  
Author(s):  
Marcus C. Christiansen ◽  
Andreas Niemeyer ◽  
Lucia Teigiszerová
Keyword(s):  
Mortality Rates ◽  
Modeling And Forecasting ◽  
Dependent Mortality

Modeling and forecasting mortality rates

2013 ◽  
Vol 52 (2) ◽  
pp. 275-285 ◽  
Author(s):  
Daniel Mitchell ◽  
Patrick Brockett ◽  
Rafael Mendoza-Arriaga ◽  
Kumar Muthuraman
Keyword(s):  
Mortality Rates ◽  
Modeling And Forecasting

BIAS-CORRECTED INFERENCE FOR A MODIFIED LEE–CARTER MORTALITY MODEL

2019 ◽  
Vol 49 (2) ◽  
pp. 433-455 ◽  
Author(s):  
Qing Liu ◽  
Chen Ling ◽  
Deyuan Li ◽  
Liang Peng
Keyword(s):  
Unit Root ◽  
Normal Limit ◽  
Mortality Rates ◽  
American Statistical Association ◽  
Mortality Model ◽  
Mortality Index ◽  
The Us ◽  
Modeling And Forecasting ◽  
Near Unit Root ◽  
Carter Model

AbstractAs a benchmark mortality model in forecasting future mortality rates and hedging longevity risk, the widely employed Lee–Carter model (Lee, R.D. and Carter, L.R. (1992) Modeling and forecasting U.S. mortality. Journal of the American Statistical Association, 87, 659–671.) suffers from a restrictive constraint on the unobserved mortality index for ensuring model’s identification and a possible inconsistent inference. Recently, a modified Lee–Carter model (Liu, Q., Ling, C. and Peng, L. (2018) Statistical inference for Lee–Carter mortality model and corresponding forecasts. North American Actuarial Journal, to appear.) removes this constraint and a simple least squares estimation is consistent with a normal limit when the mortality index follows from a unit root or near unit root AR(1) model with a nonzero intercept. This paper proposes a bias-corrected estimator for this modified Lee–Carter model, which is consistent and has a normal limit regardless of the mortality index being a stationary or near unit root or unit root AR(1) process with a nonzero intercept. Applications to the US mortality rates and a simulation study are provided as well.

Inequalities and Impact of Socioeconomic-Cultural Factors in Suicide Rates Across Italy

Crisis ◽  
2011 ◽  
Vol 32 (4) ◽  
pp. 178-185 ◽  
Author(s):  
Maurizio Pompili ◽  
Marco Innamorati ◽  
Monica Vichi ◽  
Maria Masocco ◽  
Nicola Vanacore ◽  
...  
Keyword(s):  
Suicide Rate ◽  
Age Groups ◽  
Premature Death ◽  
Mortality Rates ◽  
Mortality Data ◽  
Italian Population ◽  
Comprehensive Overview ◽  
Suicide Rates ◽  
Younger Age ◽  
Age And Sex

Background: Suicide is a major cause of premature death in Italy and occurs at different rates in the various regions. Aims: The aim of the present study was to provide a comprehensive overview of suicide in the Italian population aged 15 years and older for the years 1980–2006. Methods: Mortality data were extracted from the Italian Mortality Database. Results: Mortality rates for suicide in Italy reached a peak in 1985 and declined thereafter. The different patterns observed by age and sex indicated that the decrease in the suicide rate in Italy was initially the result of declining rates in those aged 45+ while, from 1997 on, the decrease was attributable principally to a reduction in suicide rates among the younger age groups. It was found that socioeconomic factors underlined major differences in the suicide rate across regions. Conclusions: The present study confirmed that suicide is a multifaceted phenomenon that may be determined by an array of factors. Suicide prevention should, therefore, be targeted to identifiable high-risk sociocultural groups in each country.

Global Suicide Mortality Rates – A Light at the End of the Tunnel?

Crisis ◽  
2012 ◽  
Vol 33 (5) ◽  
pp. 249-253 ◽  
Author(s):  
José Manoel Bertolote ◽  
Diego De Leo
Keyword(s):  
Mortality Rates ◽  
Suicide Mortality
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