A new relational method for smoothing and projecting age-specific fertility rates: TOPALS

Beer, Joop de

doi:10.4054/demres.2011.24.18

Cited by 23 publications

(15 citation statements)

References 35 publications

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“…This implies that the slope of the spline is assumed to equal zero for ages 0-20. The values of a and b j are determined in such a way that the values of the spline at the knots equal the observed values (De Beer, 2011).…”

Section: Topals and Partial Adjustment Modelmentioning

confidence: 99%

Smoothing and projecting age-specific probabilities of death by TOPALS

Beer¹

2012

DemRes

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BACKGROUNDTOPALS is a new relational model for smoothing and projecting age schedules. The model is operationally simple, flexible, and transparent. OBJECTIVEThis article demonstrates how TOPALS can be used for both smoothing and projecting age-specific mortality for 26 European countries and compares the results of TOPALS with those of other smoothing and projection methods. METHODSTOPALS uses a linear spline to describe the ratios between the age-specific death probabilities of a given country and a standard age schedule. For smoothing purposes I use the average of death probabilities over 15 Western European countries as standard, whereas for projection purposes I use an age schedule of 'best practice' mortality. A partial adjustment model projects how quickly the death probabilities move in the direction of the best-practice level of mortality. RESULTSOn average, TOPALS performs better than the Heligman-Pollard model and the Brass relational method in smoothing mortality age schedules. TOPALS can produce projections that are similar to those of the Lee-Carter method, but can easily be used to produce alternative scenarios as well. This article presents three projections of life expectancy at birth for the year 2060 for 26 European countries. The Baseline scenario assumes a continuation of the past trend in each country, the Convergence scenario assumes that there is a common trend across European countries, and the Acceleration scenario assumes that the future decline of death probabilities will exceed that in the past. The Baseline scenario projects that average European life expectancy at birth will 1 Netherlands Interdisciplinary Demographic Institute. E-mail: beer@nidi.nl.de Beer: Smoothing and projecting age-specific probabilities of death by TOPALS 544http://www.demographic-research.org increase to 80 years for men and 87 years for women in 2060, whereas the Acceleration scenario projects an increase to 90 and 93 years respectively. CONCLUSIONSTOPALS is a useful new tool for demographers for both smoothing age schedules and making scenarios.

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Section: Topals and Partial Adjustment Modelmentioning

confidence: 99%

Smoothing and projecting age-specific probabilities of death by TOPALS

Beer¹

2012

DemRes

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“…The main approaches for modeling the age patterns of demographic components include the imposition of empirical tables obtained from other countries and historical settings (Coale and Demeny 1966), parametric model schedules (Coale and McNeil 1972;Coale and Trussell 1974;Heligman and Pollard 1980;Knudsen et al 1993;Rogers 1986;Rogers and Castro 1981;Rogers et al 1978), relational models (Brass 1974), functional models (De Beer 2011;Hyndman and Booth 2008;Hyndman and Ullah 2007;Lee and Carter 1992), and hierarchical Bayesian models (Czado et al 2005;Girosi and King 2008). Of these, the most successful and widely used for forecasting future patterns and uncertainty have been the relational and functional models-particularly, the Lee-Carter model for mortality (Booth and Tickle 2008).…”

Section: Background Forecasting the Age Patterns Of Fertility Mortalmentioning

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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“…Brass (1974Brass ( , 1978 developed a linear relationship between a double logarithmic transformation of the age patterns as well as the standard age schedules. However, the limitation of this model is the lack of interpretations of the demographic parameters (Beer 2011).…”

Section: Introductionmentioning

confidence: 99%

“…The skew-symmetric probability density function was applied for this purpose by Mazzuco and Scarpa (2011). The piece-wise quadratic splines function proposed by Schmertmann (2003) with 13 parameters was found good fit to the wide variety of fertility data, however, it does not include all parameters, lack of interpretations and applications for further fertility projection (Beer 2011). In this context, the Inverse Gaussian Probability Distribution (IGPD) model has been applied to describe the distribution of ASFR pattern among Nepalese mothers, which is considered as the function of age of women of child bearing age of 15-49 years. The IGPD model has only three parameters (α, µ and λ).…”

Section: Introductionmentioning

confidence: 99%

“…A number of models have been developed to study fertility performance (Aryal 2005, Aryal & Paudel 2012, Brass 1974, 1978. There were several limitations that the discussed models were not good fit to the data of bi-modality and uni-modality (Beer 2011). The skew-symmetric probability density function was applied for this purpose by Mazzuco and Scarpa (2011).…”

Section: Introductionmentioning

confidence: 99%

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Inverse Gaussian Model to Describe the Distribution of Age Specific Fertility Rates of Nepal

Gaire

Aryal

2015

J. Inst. Sci. Tech.

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This paper attempts to formulate Inverse Gaussian probability model to describe the distribution of the age specific fertility rates among Nepalese mothers. The average age of child bearing has been found to be 26.12 years whereas child bearing starts at the age of 15.82 years. Estimated total fertility rate (TFR) has been found to be 2.60 against the observed value of 2.6 per woman. The coefficient of determination and chi-square values suggest that the model found to be good fit to the Nepal Demographic and Health Surveys data. Findings may help researchers and policy makers for applying the model in order to obtain the descriptive statistics for policy interventions.

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A new relational method for smoothing and projecting age-specific fertility rates: TOPALS

Cited by 23 publications

References 35 publications

Smoothing and projecting age-specific probabilities of death by TOPALS

Smoothing and projecting age-specific probabilities of death by TOPALS

Bayesian Population Forecasting: Extending the Lee-Carter Method

Inverse Gaussian Model to Describe the Distribution of Age Specific Fertility Rates of Nepal

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