Estimation of multi-state life table functions and their variability from complex survey data using the SPACE Program
AbstractThe multistate life table (MSLT) model is an important demographic method to document life cycle processes. In this study, we present the SPACE (Stochastic Population Analysis for Complex Events) program to estimate MSLT functions and their sampling variability. It has several advantages over other programs, including the use of microsimulation and the bootstrap method to estimate the sampling variability. Simulation enables researchers to analyze a broader array of statistics than the deterministic approach, and may be especially advantageous in investigating distributions of MSLT functions. The bootstrap method takes sample design into account to correct the potential bias in variance estimates.
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Bibliographic InfoArticle provided by Max Planck Institute for Demographic Research, Rostock, Germany in its journal Demographic Research.
Volume (Year): 22 (2010)
Issue (Month): 6 (January)
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Web page: http://www.demogr.mpg.de/
bootstrap; health expectancy; multi-state life table; population aging;
Find related papers by JEL classification:
- J1 - Labor and Demographic Economics - - Demographic Economics
- Z0 - Other Special Topics - - General
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- Liming Cai & Nathaniel Schenker & James Lubitz, 2006. "Analysis of functional status transitions by using a semi-Markov process model in the presence of left-censored spells," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(4), pages 477-491.
- Agnes Lievre & Nicolas Brouard & Christopher Heathcote, 2003. "The Estimation Of Health Expectancies From Cross-Longitudinal Surveys," Mathematical Population Studies, Taylor & Francis Journals, vol. 10(4), pages 211-248.
- Dustin Brown & Mark Hayward & Jennifer Montez & Robert Hummer & Chi-Tsun Chiu & Mira Hidajat, 2012. "The Significance of Education for Mortality Compression in the United States," Demography, Springer, vol. 49(3), pages 819-840, August.
- Jennifer Van Hook & Claire Altman, 2013. "Using Discrete-Time Event History Fertility Models to Simulate Total Fertility Rates and Other Fertility Measures," Population Research and Policy Review, Springer, vol. 32(4), pages 585-610, August.
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