Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Age-Period-Cohort Model× | Lī-Kārtera modelis× | |
|---|---|---|
| Nozare | Demogrāfija | Demogrāfija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1983 | 1992 |
| Autors≠ | Theodore R. Holford (modern estimable-function formulation) | Ronald Lee & Lawrence Carter |
| Tips≠ | Regression decomposition of rates into age, period and cohort effects | Stochastic mortality forecasting model |
| Pirmavots≠ | Holford, T. R. (1983). The estimation of age, period and cohort effects for vital rates. Biometrics, 39(2), 311–324. DOI ↗ | Lee, R. D., & Carter, L. R. (1992). Modeling and forecasting U.S. mortality. Journal of the American Statistical Association, 87(419), 659–671. DOI ↗ |
| Citi nosaukumi≠ | APC Model, Age-Period-Cohort Analysis, Holford APC Model | LC Model, Lee-Carter Mortality Model, Singular Value Decomposition Mortality Model, Lee-Carter Ölümlülük Modeli |
| Saistītās≠ | 4 | 2 |
| Kopsavilkums≠ | The age-period-cohort (APC) model decomposes variation in a vital rate — mortality, incidence, fertility — into three temporal dimensions: the age of individuals, the calendar period of observation, and the birth cohort to which they belong. It is the standard framework for asking whether a trend reflects how risk changes with age, contemporaneous period influences affecting all ages at once, or generational effects carried by successive cohorts. Its defining technical challenge is that cohort equals period minus age, an exact linear dependence that makes the three sets of linear effects unidentifiable without further assumptions; Holford's 1983 formulation clarified exactly which quantities can and cannot be estimated. | The Lee-Carter model is a stochastic framework for modeling and forecasting age-specific mortality rates, introduced by Ronald Lee and Lawrence Carter in their landmark 1992 paper. It decomposes the logarithm of age-specific death rates into an age pattern of mortality, a time-varying index of mortality level, and an age-specific sensitivity of that index, then forecasts the time index using ARIMA time-series methods to generate probabilistic mortality projections. |
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