the double-gap life expectancy forecasting model · 5/27/2016 · forecast: female and male life...

The Double-Gap Life Expectancy Forecasting Model

Marius Pascariu

Max-Planck Odense Center on the Biodemography of AgingUniversity of Southern Denmark

[email protected]

IAA Mortality Working Group meeting, St.Petersburg

May 27, 2016

Pascariu, Canudas-Romo & Vaupel (MaxO) DGM May 27, 2016 1 / 19

Classification of the forecasting models

Expectations

Used in the form of expert opinions, targeting of life expectancy andscenarios.

Extrapolative methods

Assume that future trends will essentially be a continuation of the past.

Explanatory methods

Are based on structural or causal epidemiological models of certain causesof death involving known risk factors.


The Double-Gap Life Expectancy Forecasting Model- Pascariu M., Canudas-Romo V. and Vaupel J.W.


Objectives

Better forecasts

Female and male population modelled together

International context recognition


Data

Human Mortality Database (2015)

Historical period: 1950 - 2010

Number of countries: 38


Record life expectancy at birth


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Iceland, 1950, 73.5

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Japan, 2010, 86.3

Forecast: Female and male life expectancy at birth, USA, 1950-2050




Marius.Pascariu

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Forecast: Female and male life expectancy at age 65, USA, 1950-2050


Results

Forecasts of life expectancy in 2050, with 80% prediction intervals. Themodels were evaluated on data from the period 1950-2010.

AGE 0 AGE 65

Model Indicator Females Males Sex gap Females Males Sex gap

DGMex ,2050 89.58 86.28 3.30 25.53 22.97 2.56

80%PI (88.56-90.59) (84.9-87.62) (1.95-4.68) (24.73-26.25) (21.77-24.16) (1.37-3.76)

LCex ,2050 86.39 81.86 4.53 24.17 21.12 3.05

80%PI (85.67-87.19) (81.23-82.47) - (23.38-24.94) (20.58-21.65) -

CBDex ,2050 - - - 24.39 21.36 3.03

80%PI - - - (23.29-25.58) (20.47-22.27) -

ex ,2010 81.22 76.38 4.84 20.52 17.91 2.61


Forecasting gaps

D-gap: The gaps to best practice trend

OdDk,x ,t = µk,x︸︷︷︸Drift

+

p∑i=1

φiOdDk,x ,t−i︸︷︷︸

Regression

+ ε(1)k,x ,t +

q∑j=1

θjε(1)k,x ,t−j︸︷︷︸

Smoothed noise

G-gap: The sex-gap

G ∗k,x ,t =

β0 + β1Gk,x ,t−1 + β2Gk,x ,t−2︸︷︷︸Autoregresive model

+ β3(efk,x ,t − τ)+︸︷︷︸Level associated with

life expectancywhere the gapstarts narrowing

+ε(2)k,x ,t ,

Gk,x ,t−1 + ε(3)k,x ,t︸︷︷︸

Random walk

for efk,x ,t > A.


The gaps at age 0 and 65


Accuracy

Average accuracy over four evaluation periods, computed based on predictions of the lifeexpectancy at birth and at age 65. Evaluation periods: 1985-2010, 1990-2010, 1995-2010 and2000-2010.

ME MAPE

Country Model AGE 0 AGE 65 AGE 0 AGE 65

FEMALES

USA

DGM -0.7990 -0.2950 1.00% 2.01%

LC -0.4958 -0.3889 0.71% 2.68%

CBD - -0.4031 - 2.70%

38 HMD

DGM -0.2161 0.4132 1.21% 3.22%

LC 0.3264 0.3407 0.89% 3.07%

CBD - 0.3320 - 3.12%

MALES USA

DGM 0.1997 0.7989 0.39% 4.71%

LC 0.7682 0.7083 1.04% 4.16%

CBD - 0.6804 - 3.99%

38 HMD

DGM 0.0942 0.7643 1.76% 5.41%

LC 1.2874 0.8037 2.21% 5.79%

CBD - 0.7806 - 5.69%

.

ME =

mean(ωk,x,t

)MAPE =

mean(|100×

ωk,x,t

ek,x,t|)


Conclusions

The current approach combines separate forecasts to obtain the maleand female life expectancy levels

The results are coherent with the best-practice trend and correlated

The forecasting model proves robustness

Back-testing results are excellent!

vsdfsWhat’s next:

Decomposition of life expectancy into age-specific death rates


Thank you!


the double-gap life expectancy forecasting model · 5/27/2016 · forecast: female and male life...

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