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A system of model fertility schedules with graphically intuitive parameters

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Author Info

  • Carl Schmertmann

    (Florida State University)

Abstract

I propose and examine a new family of models for age-specific fertility schedules, in which three index ages determine the schedule's shape. The new system is based on constrained quadratic splines. It has easily interpretable parameters, is flexible enough to fit a variety of "noiseless" schedules well, and is inflexible enough to avoid implausible estimates from noisy data. Across a set of over two hundred contemporary ASFR schedules, the new model fits a majority better, and in some cases much better, than the Coale-Trussell model. When fit to a recent Swedish time series, model parameters exhibit simple, regular changes over time, suggesting utility in forecasting applications. In simulated small-sample data the new model produces plausible ASFR estimates, with errors similar to Coale-Trussell.

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File URL: http://www.demographic-research.org/volumes/vol9/5/9-5.pdf
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Bibliographic Info

Article provided by Max Planck Institute for Demographic Research, Rostock, Germany in its journal Demographic Research.

Volume (Year): 9 (2003)
Issue (Month): 5 (October)
Pages: 81-110

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Handle: RePEc:dem:demres:v:9:y:2003:i:5

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Web page: http://www.demogr.mpg.de/

Related research

Keywords: age-specific fertility; age-specific rates; Coale-Trussell; estimation; fertility; models; splines;

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References

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  1. Jan Hoem & Dan Madien & Jørgen Nielsen & Else-Marie Ohlsen & Hans Hansen & Bo Rennermalm, 1981. "Experiments in modelling recent Danish fertility curves," Demography, Springer, vol. 18(2), pages 231-244, May.
  2. Ansley Coale & A. John & Toni Richards, 1985. "Calculation of age-specific fertility schedules from tabulations of parity in two censuses," Demography, Springer, vol. 22(4), pages 611-623, November.
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Citations

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Cited by:
  1. Renato Assunção & Carl Schmertmann & Joseph Potter & Suzana Cavenaghi, 2005. "Empirical bayes estimation of demographic schedules for small areas," Demography, Springer, vol. 42(3), pages 537-558, August.
  2. Ezra Gayawan & Samson Adebayo & Reuben A. Ipinyomi & Benjamin Oyejola, 2010. "Modeling fertility curves in Africa," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 22(10), pages 211-236, February.
  3. Mikko Myrskylä & Joshua R. Goldstein & Yen-hsin Alice Cheng, 2012. "New cohort fertility forecasts for the developed world," MPIDR Working Papers WP-2012-014, Max Planck Institute for Demographic Research, Rostock, Germany.
  4. Anastasia Kostaki & Paraskevi Peristera, 2007. "Modeling fertility in modern populations," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 16(6), pages 141-194, March.
  5. Rueda, Cristina & Rodríguez, Pilar, 2010. "State space models for estimating and forecasting fertility," International Journal of Forecasting, Elsevier, vol. 26(4), pages 712-724, October.
  6. Anastasia Kostaki & Javier Moguerza & Alberto Olivares & Stelios Psarakis, 2009. "Graduating the age-specific fertility pattern using Support Vector Machines," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 20(25), pages 599-622, June.
  7. Marc Luy, 2012. "Estimating Mortality Differences in Developed Countries From Survey Information on Maternal and Paternal Orphanhood," Demography, Springer, vol. 49(2), pages 607-627, May.
  8. Gianni Corsetti & Marco Marsili, 2013. "Previsioni stocastiche della popolazione nell’ottica di un Istituto Nazionale di Statistica," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 15(2-3), pages 5-29.

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