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      Lee-Carter method for forecasting mortality for Peruvian Population

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          Abstract

          In this article, we have modeled mortality rates of Peruvian female and male populations during the period of 1950-2017 using the Lee-Carter (LC) model. The stochastic mortality model was introduced by Lee and Carter (1992) and has been used by many authors for fitting and forecasting the human mortality rates. The Singular Value Decomposition (SVD) approach is used for estimation of the parameters of the LC model. Utilizing the best fitted auto regressive integrated moving average (ARIMA) model we forecast the values of the time dependent parameter of the LC model for the next thirty years. The forecasted values of life expectancy at different age group with \(95\%\) confidence intervals are also reported for the next thirty years. In this research we use the data, obtained from the Peruvian National Institute of Statistics (INEI).

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          A cohort-based extension to the Lee–Carter model for mortality reduction factors

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            A Two-Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration

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              Deceleration in the age pattern of mortality at older ages.

              The rate of mortality increase with age tends to slow down at very old ages. One explanation proposed for this deceleration is the selective survival of healthier individuals to older ages. Data on mortality in Sweden and Japan are generally compatible with three predictions of this hypothesis: (1) decelerations for most major causes of death; (2) decelerations starting at younger ages for more "selective" causes; and (3) a shift of the deceleration to older ages with declining levels of mortality. A parametric model employed to illustrate the third prediction relies on the distinction between senescent and background mortality. This dichotomy, though simplistic, helps to explain the observed timing of the deceleration.
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                Author and article information

                Journal
                22 November 2018
                Article
                1811.09622
                72c3a002-e6a1-4b4d-9ced-7e4244074aab

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                16 pages, 6 figures
                econ.GN q-fin.EC

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