Comparing strategies for matching mortality
forecasts to the most recently observed data
What is the best trade-off between short-term accuracy and longterm robustness?
Work in progress
Lenny Stoeldraijer
Introduction
– Every year: population forecast
‐ thus also every year a new mortality forecast
– Goal: Produce a mortality forecast that is
‐ Accurate for the short-term
‐ Robust for the long-term
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Introduction
– Objective: to evaluate different options to adjust the
forecasted values to recently observed and newly
observed value, in order to determine the best method
to preserve both short-term accuracy, long-term
robustness and plausibility.
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Overview
– Data and methods
– Results
– (some) Summary
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Data & Methods (1)
– Data from the Human Mortality Database, 1950-2009
– Countries: The Netherlands, Finland, France, Italy,
Sweden, Spain, Denmark, Norway
– Lee-Carter method (Singular Value Decomposition)
– Pseudo-forecasts: ‘out-of-sample’ forecasts for years
already observed (i.e. fitting period 1950-1980 and projection period
1980-2009, then fitting period 1951-1981 and projection period 1981-2009,
etc.)
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Data & Methods (2)
– Options for incorporating adjustment to recently
observed and new values in the forecasting
methodology:
‐ Jump-off rates equal to model values
‐ Jump-off rates equal to most recent observed values
‐ Jump-off rates equal to an average of recent observed
values
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Short-term
– Accurate!
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Results – Short-term (1)
– Average error in E0 in jump-off year:
Men
Women
Model M
Average observed M
Observed M
Model F
Average observed F
Observed F
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Norway
Denmark
Spain
Sweden
Italy
France
Finland
The Netherlands
Norway
Denmark
Spain
Sweden
Italy
France
Finland
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.5
The Netherlands
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
Results – Short-term (2)
– Average error in E0 in first year of projection period:
Men
Women
Model M
Average observed M
Observed M
Model F
Average observed M
Observed F
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Norway
Denmark
Spain
Sweden
Italy
France
Finland
The Netherlands
Norway
Denmark
Spain
Sweden
Italy
France
Finland
0.5
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.5
The Netherlands
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
-0.8
Results – Short-term (3)
– Average error in E0 in tenth year of projection period:
Men
Women
0.5
0.0
-0.5
-1.0
-1.5
-2.0
-2.5
-3.0
1.5
1.0
0.5
0.0
-0.5
-1.0
Model M
Average observed M
Observed M
Model F
Average observed F
Observed F
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Norway
Denmark
Spain
Sweden
Italy
France
Finland
The Netherlands
Norway
Denmark
Spain
Sweden
Italy
France
Finland
The Netherlands
-1.5
Summary: Short-term
– Taking the most recent observed values as jump-off rates
results in the smallest error on the short-term
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Long-term
– Robust!
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Results – Long-term (1)
Life expectancy in 2050
Life expectancy in 2050
83
83
78
78
73
73
68
68
1980
1985
1990
1995
2000
2005
1980
1985
1990
Jump-off year
1995
2000
2005
Jump-off year
NL Model
NL Observed
FI Model
FI Observed
NL Average observed
NL Actual E0 in jump-off year
FI Average observed
FI Actual E0 in jump-off year
Difference
Difference
2.5
2.5
1.5
1.5
0.5
0.5
-0.5
-0.5
-1.5
-1.5
1980
1985
1990
1995
2000
2005
1980
1985
1990
Jump-off year
1995
2000
2005
Jump-off year
NL Model
NL Observed
FI Model
FI Observed
NL Average observed
NL Actual E0 in jump-off year
FI Average observed
FI Actual E0 in jump-off year
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Results – Long-term (2)
– Average alteration for 2050 with new data:
Men
Women
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
1.5
1.0
0.5
0.0
-0.5
-1.0
Model M
Average observed M
Observed M
Model F
Average observed M
Observed F
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Norway
Denmark
Spain
Sweden
Italy
France
Finland
The Netherlands
Norway
Denmark
Spain
Sweden
Italy
France
Finland
The Netherlands
-1.5
Summary: Long-term
– Taking an average of the most recent observed values as
jump-off rates results in the smallest error on the longterm
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Further work
–
–
–
–
The best combination
Other models
More countries
Other measures
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Thank you!
Questions?
[email protected]
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