Editing Life expectancy (section)

==Forecasting==
Forecasting life expectancy and mortality form an important subdivision of [[demography]]. Future trends in life expectancy have huge implications for old-age support programs (like [[U.S. Social Security]] and [[pension]]) since the cash flow in these systems depends on the number of recipients who are still living (along with the rate of return on the investments or the tax rate in [[pay-as-you-go tax|pay-as-you-go]] systems). With longer life expectancies, the systems see increased cash outflow; if the systems underestimate increases in life-expectancies, they will be unprepared for the large payments that will occur, as humans live longer and longer.

Life expectancy forecasting is usually based on one of two different approaches:

# Forecasting the life expectancy directly, generally using [[ARIMA]] or other time-series extrapolation procedures. This has the advantage of simplicity, but it cannot account for changes in mortality at specific ages, and the forecast number cannot be used to derive other [[life table]] results. Analyses and forecasts using this approach can be done with any common statistical/mathematical software package, like [[EViews]], [[R (programming language)|R]], [[SAS (software)|SAS]], [[Stata]], [[Matlab]], or [[SPSS]].
# Forecasting age-specific [[death rates]] and computing the life expectancy from the results with life table methods. This is usually more complex than simply forecasting life expectancy because the analyst must deal with correlated age-specific mortality rates, but it seems to be more robust than simple one-dimensional [[time series]] approaches. It also yields a set of age-specific rates that may be used to derive other measures, such as survival curves or life expectancies at different ages. The most important approach in this group is the [[Lee-Carter model]],<ref>{{cite web|url=http://www.soa.org/library/journals/north-american-actuarial-journal/2000/january/naaj0001_5.pdf|title=The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications – SOA|publisher=SOA|access-date=9 April 2018|archive-date=7 March 2019|archive-url=https://web.archive.org/web/20190307054148/https://www.soa.org/library/journals/north-american-actuarial-journal/2000/january/naaj0001_5.pdf|url-status=dead}}</ref> which uses the [[singular value decomposition]] on a set of transformed age-specific mortality rates to reduce their dimensionality to a single time series, forecasts that time series, and then recovers a full set of age-specific mortality rates from that forecasted value. The software includes Professor [[Rob J. Hyndman]]'s [https://web.archive.org/web/20100822170736/http://robjhyndman.com/software/demography/ R package called 'demography'] and [http://lcfit.demog.berkeley.edu/ UC Berkeley's LCFIT system].