1 / 40

Smoothing mortality rates using R

Smoothing mortality rates using R. Gary Brown & Julie Mills. Overview. Introduction Context Methodology Implementation Results Summary What’s next?. Introduction . Future mortality rates are published every two years Until 2004, by the Government Actuary’s Department (GAD)

beck-howe
Download Presentation

Smoothing mortality rates using R

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Smoothing mortality rates using R Gary Brown & Julie Mills

  2. Overview • Introduction • Context • Methodology • Implementation • Results • Summary • What’s next?

  3. Introduction • Future mortality rates are published every two years • Until 2004, by the Government Actuary’s Department (GAD) • Since 2006, by ONS (with GAD consultants) • The methodology was designed by GAD in the 1990s and runs in Excel • In 2010, ONS reviewed the current process • Implementation and testing still ongoing

  4. Context • Mortality rates, estimated 75 years into the future, are a key factor in National Population Projections (others: births and net migration) • Natural change (births – deaths) accounts for 1/3 of total population change • Population projections used as inputs/control totals for other government projections, such as numbers of school children or pensioners • Robustness of mortality rates is crucial

  5. Methodology - current Mortality rate = deaths/pop

  6. Methodology - current constrained survivor ratio Mortality rate = deaths/pop

  7. Methodology - current constrained survivor ratio Mortality rate = deaths/pop Smooth within years (to 103/104)

  8. Methodology - current constrained survivor ratio Mortality rate = deaths/pop extrapolate to age 120 Smooth within years (to 103/104)

  9. Methodology - current constrained survivor ratio Mortality rate = deaths/pop extrapolate to age 120 Smooth within years (to 103/104) Estimate year T+1 for each age

  10. Methodology - current constrained survivor ratio Mortality rate = deaths/pop extrapolate to age 120 Smooth within years (to 103/104) exponential smoothingx2 Estimate year T+1 for each age

  11. Methodology - current constrained survivor ratio Mortality rate = deaths/pop extrapolate to age 120 Smooth within years (to 103/104) exponential smoothingx2 Estimate year T+1 for each age Smooth improvement rate in T+1

  12. Methodology - current constrained survivor ratio Mortality rate = deaths/pop extrapolate to age 120 Smooth within years (to 103/104) exponential smoothingx2 Estimate year T+1 for each age 1x1 3x1 5x1 3x1 1x1 MAs Smooth improvement rate in T+1

  13. Methodology - current constrained survivor ratio Mortality rate = deaths/pop extrapolate to age 120 Smooth within years (to 103/104) exponential smoothingx2 Estimate year T+1 for each age 1x1 3x1 5x1 3x1 1x1 MAs Smooth improvement rate in T+1 Improvement rates up to T+26

  14. Methodology - current constrained survivor ratio Mortality rate = deaths/pop extrapolate to age 120 Smooth within years (to 103/104) exponential smoothingx2 Estimate year T+1 for each age 1x1 3x1 5x1 3x1 1x1 MAs Smooth improvement rate in T+1 T+26 expert opinions Improvement rates up to T+26

  15. Methodology - current constrained survivor ratio Mortality rate = deaths/pop extrapolate to age 120 Smooth within years (to 103/104) exponential smoothingx2 Estimate year T+1 for each age 1x1 3x1 5x1 3x1 1x1 MAs Smooth improvement rate in T+1 T+26 expert opinions Improvement rates up to T+26 Mortality rates for T+1 to T+26

  16. Methodology - current constrained survivor ratio Mortality rate = deaths/pop extrapolate to age 120 Smooth within years (to 103/104) exponential smoothingx2 Estimate year T+1 for each age 1x1 3x1 5x1 3x1 1x1 MAs Smooth improvement rate in T+1 T+26 expert opinions Improvement rates up to T+26 … further adjustments Mortality rates for T+1 to T+26

  17. Methodology - current constrained survivor ratio Mortality rate = deaths/pop extrapolate to age 120 Smooth within years (to 103/104) exponential smoothingx2 Estimate year T+1 for each age 1x1 3x1 5x1 3x1 1x1 MAs Smooth improvement rate in T+1 T+26 expert opinions Improvement rates up to T+26 … further adjustments Mortality rates for T+1 to T+26

  18. Methodology - new • Replace two-stage smoothing process • Smooth mortality rates surface simultaneously over ages and years • Estimate improvement rate using existing smoothed years – ie do not estimate T+1 • Requires longer path to T+26 opinions!

  19. Methodology – 2 dimensional p-spline • Thoroughly tested, and recommended, by Continuous Mortality Investigation

  20. Methodology – 2 dimensional p-spline • Thoroughly tested, and recommended, by Continuous Mortality Investigation

  21. Methodology – 2 dimensional p-spline • Thoroughly tested, and recommended, by Continuous Mortality Investigation

  22. Methodology – 2 dimensional p-spline • Thoroughly tested, and recommended, by Continuous Mortality Investigation • Best advice - read “Smoothing and forecasting mortality rates”, Currie et al, 2004!

  23. Implementation • Difficult to understand (and explain) … but easy to implement!

  24. Implementation • Difficult to understand (and explain) … but easy to implement! • MortalitySmooth (Carlo G Camarda) in R

  25. Implementation • Difficult to understand (and explain) … but easy to implement! • MortalitySmooth (Carlo G Camarda) in R Mort2Dsmooth(x=ages,y=years,Z=deaths,offset=log(pop))

  26. Implementation • Difficult to understand (and explain) … but easy to implement! • MortalitySmooth (Carlo G Camarda) in R Mort2Dsmooth(x=ages,y=years,Z=deaths,offset=log(pop)) • Smoothed values = 21st entry in list of R output

  27. Results - testing

  28. Results - testing 61-04 61-05 61-09 61-06 61-07 61-08 10 20 30 40 50 60 70 80 90 0 Mortality improvement rates by age, 2003/04 6 5 % 4 3 2 1 0 102 Age

  29. Results – mortality rates in the base year Issues • New method does not project rates forward to base year • Edge effects Solution • Step back 2 years into the data set 2010 mortality rates = 2007 mortality rates x (1 – 2006-07 improvement rates/100) ^ 3

  30. Results – mortality rates in the base year 100 52 yrs 49 48 48 yrs 0 1961………………………………………………….. 2007 2008 2009 2010 Age Year

  31. Past improvements in smoothed mortality rates, males – old method

  32. Past improvements in smoothed mortality rates, males – new method

  33. Past improvements in smoothed mortality rates, Scotland males – new method

  34. Past improvements in smoothed mortality rates, females – old method

  35. Past improvements in smoothed mortality rates, females – new method

  36. Comparison of projected smooth % changes in death rates by age, UK 2009-10 Males

  37. Comparison of projected smooth % changes in death rates by age, UK 2009-10 Females

  38. Comparison of actual and projected expectation of life at birth

  39. Summary • New smoothing method used to produce the 2010-based ‘proposed’ mortality assumptions • Introduced in the 2010-based consultation with devolved administrations and government departments

  40. What’s next? • More testing/evaluation: • Over-smoothing • Adding 2010 data • Derivation of base year rates • Using R to project mortality rates

More Related