Completing incomplete cohort fertility schedules

Cheng, Peng; Lin, Eric S.

doi:10.4054/demres.2010.23.9

Cited by 8 publications

(11 citation statements)

References 31 publications

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“…The threshold analysis is in line with the findings of the tests of stochastic dominance: the thresholds below which a specific percentage of errors falls are relatively small for the simple EMs ( 12 , 14 – 16 ), BAs ( 19 , 20 ), and freeze rates in comparison with those of more complex EMs ( 9 , 10 ), hybrid methods ( 8 , 26 , 27 ), and PARs ( 2 – 7 ).…”

Section: Resultssupporting

confidence: 81%

“…Regarding the method types, it is striking that the forecasts of simple EMs ( 12 , 14 – 16 ) and BAs ( 19 , 20 ) are, on average, more accurate than those of more complex EMs ( 9 , 10 ), CONs ( 22 ), and most PARs ( 2 – 7 ). In addition, the forecasts of hybrid methods that combine properties of PARs and EMs ( 26 , 27 ) or BAs ( 8 ) are, on average, more accurate than the pure PARs.…”

Section: Resultsmentioning

confidence: 99%

“…EMs fit a model to observed fertility trends and extrapolate the trajectory based on the model. Complex EMs ( 9 , 10 ) consider fertility dynamics in all three dimensions (age, calendar year, and birth cohort), whereas simple EMs ( 11 – 16 ) reduce complexity and reflect fertility trends in less detail. EMs often use time series models ( 17 ) to extrapolate trajectories ( 9 – 16 ) and to provide uncertainty estimates ( 10 – 15 ).…”

mentioning

confidence: 99%

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Forecast accuracy hardly improves with method complexity when completing cohort fertility

Bohk-Ewald

Myrskylä

2018

Proc. Natl. Acad. Sci. U.S.A.

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SignificanceInformation on cohort fertility is critical for the understanding of population dynamics, but only in historical settings can it be calculated without forecasting. Several forecasting methods exist, but their strengths and weaknesses have not been evaluated. Relying on the Human Fertility Database, the largest high-quality fertility dataset to date, and the globally representative United Nations World Population Prospects, we present an assessment of all major methods that complete cohort fertility. This analysis is crucial to advance the understanding of benefits and drawbacks of state-of-the-art methods. We analyze forecast accuracy and uncertainty quantification, identify methodological breakthroughs, and uncover unresolved issues. This study constitutes an evaluation benchmark for cohort fertility forecasting and may inspire establishment of similar evaluation benchmarks in related fields.

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Section: Resultssupporting

confidence: 81%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

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Forecast accuracy hardly improves with method complexity when completing cohort fertility

Bohk-Ewald

Myrskylä

2018

Proc. Natl. Acad. Sci. U.S.A.

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“…The extended model, denoted by F2, includes a cohort effect (see also Cheng and Lin 2010;Lee 1993Lee , 2000. The population at risk represents all women of reproductive age.…”

Section: Forecasting Fertilitymentioning

confidence: 99%

Bayesian Population Forecasting: Extending the Lee-Carter Method

et al. 2015

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In this article, we develop a fully integrated and dynamic Bayesian approach to forecast populations by age and sex. The approach embeds the Lee-Carter type models for forecasting the age patterns, with associated measures of uncertainty, of fertility, mortality, immigration, and emigration within a cohort projection model. The methodology may be adapted to handle different data types and sources of information. To illustrate, we analyze time series data for the United Kingdom and forecast the components of population change to the year 2024. We also compare the results obtained from different forecast models for age-specific fertility, mortality, and migration. In doing so, we demonstrate the flexibility and advantages of adopting the Bayesian approach for population forecasting and highlight areas where this work could be extended.

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“…They allow us to answer questions such as: when did a given woman give birth, at what age, and how many children does she have. A complementary character of such information is very important; therefore studies offer various methods of supplementing the missing information for cohort fertility schedules (Cheng and Lin, 2010).…”

Section: Introductionmentioning

confidence: 99%

Cohort Patterns of Fertility in Poland Based on Staging Process – Generations 1930-1980

Grzenda

Frątczak

2018

Statistics in Transition New Series

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As a transition country in the region of Central and Eastern Europe, Poland has experienced unprecedented changes in the fertility. Currently, the total fertility rate level is very low, ca. 1.3 children per woman, which is below the replacement level. Many studies have described changes in fertility based on the crosssectional approach. However, the changes of cohort fertility have been described not quite sufficiently. Our paper complements this gap by the assessment of stochastic fertility tables, calculated for five-year generations of women born in the period 1930-1980. The main goal of this study is to analyse changes in the cohort patterns of female fertility in Poland.

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Completing incomplete cohort fertility schedules

Cited by 8 publications

References 31 publications

Forecast accuracy hardly improves with method complexity when completing cohort fertility

Forecast accuracy hardly improves with method complexity when completing cohort fertility

Bayesian Population Forecasting: Extending the Lee-Carter Method

Cohort Patterns of Fertility in Poland Based on Staging Process – Generations 1930-1980

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