Methoden vergleichen

	Lee-Carter Mortality Model	Life Expectancy Decomposition
Fachgebiet	Demographie	Social Epidemiology
Familie	Regression model	Process / pipeline
Entstehungsjahr	1992	1984
Urheber	Ronald D. Lee & Lawrence R. Carter	Eduardo E. Arriaga; John H. Pollard
Typ	Log-bilinear model for forecasting age-specific mortality	Demographic decomposition pipeline for differences in a summary measure
Wegweisende Quelle	Lee, R. D., & Carter, L. R. (1992). Modeling and Forecasting U.S. Mortality. Journal of the American Statistical Association, 87(419), 659-671.	Arriaga, E. E. (1984). Measuring and explaining the change in life expectancies. Demography, 21(1), 83-96.
Aliasnamen	Lee-Carter Method, Log-Bilinear Mortality Model, LC Mortality Forecast, Poisson Lee-Carter Model	Life Expectancy Decomposition Methods, Decomposition of Changes in Life Expectancy, Age and Cause Decomposition of Life Expectancy, Stepwise Life Expectancy Decomposition
Verwandt	3	4
Zusammenfassung	The Lee-Carter model is the benchmark method for forecasting human mortality. Introduced by Ronald Lee and Lawrence Carter in 1992 for U.S. data, it captures the entire schedule of age-specific death rates with a remarkably parsimonious structure: the logarithm of the death rate at each age is a fixed average age profile, plus an age-specific sensitivity multiplied by a single time index that summarizes the overall level of mortality in each year. Because mortality has fallen steadily across the twentieth century, this single index trends downward over time, and forecasting it as a simple time-series process, typically a random walk with drift, propagates the historical pace of improvement into the future for every age at once. Brouhns, Denuit, and Vermunt later recast the fitting step as a Poisson regression on observed death counts, giving the model a proper statistical likelihood and more reliable uncertainty, and the approach now anchors official population and pension projections worldwide.	Life-expectancy decomposition answers a question that a single number cannot: when life expectancy rises over time, or differs between two populations, exactly which ages and which causes of death are responsible? The family of methods takes two life tables and splits their gap in e0 (or ex at any age) into additive contributions from mortality differences in each age interval, with the contributions summing exactly to the total gap. Eduardo Arriaga's 1984 stepwise discrete method became the field standard because it is exact, intuitive, and easy to extend to a cause-of-death breakdown, separating a 'direct' effect of changed survival within an interval from an 'indirect plus interaction' effect that the change propagates to later ages. John Pollard's continuous formulation expresses the same decomposition as an integral of age-specific mortality differences weighted by their leverage on life expectancy, providing the theoretical underpinning and a cross-check. This page treats the general decomposition pipeline; the dedicated Arriaga and Pollard pages cover each estimator in depth.