Volume 30 - Article 1 | Pages 1–48
An application of MCMC simulation in mortality projection for populations with limited data
By Jackie Li
Abstract
Objective: IIn this paper, we investigate the use of Bayesian modeling and Markov chain Monte Carlo (MCMC) simulation, via the software WinBUGS, to project future mortality for populations with limited data. In particular, we adapt some extensions of the Lee-Carter method under the Bayesian framework to allow for situations in which mortality data are scarce. Our approach would be useful for certain developing nations that have not been regularly collecting death counts and population statistics. Inferences of the model estimates and forecasts can readily be drawn from the simulated samples. Information on another population resembling the population under study can be exploited and incorporated into the prior distributions in order to facilitate the construction of probability intervals. The two sets of data can also be modeled in a joint manner. We demonstrate an application of this approach to some data from China and Taiwan.
Author’s Affiliation
Most recent similar articles in Demographic Research
A Bayesian model for age at death with cohort effects
Volume 51 - Article 33
| Keywords:
age at death,
Bayesian approach,
cohort effects,
Italy,
mortality
A multidimensional global migration model for use in cohort-component population projections
Volume 51 - Article 11
| Keywords:
age dependency,
education,
international migration,
migration,
modelling,
population projection,
projections
Epilocal: A real-time tool for local epidemic monitoring
Volume 44 - Article 12
| Keywords:
COVID-19,
modelling,
pandemic,
Poisson regression
A Bayesian analysis of the spatial concentration of individual wealth in the US North during the nineteenth century
Volume 30 - Article 36
| Keywords:
Bayesian approach,
historical,
wealth
Another 'futile quest'? A simulation study of Yang and Land's Hierarchical Age-Period-Cohort model
Volume 30 - Article 11
| Keywords:
age-period-cohort models,
identification problem,
Markov chain Monte Carlo (MCMC),
simulation,
Yang and Land
Cited References: 18
Download to Citation Manager
PubMed
Google Scholar