Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models

Hyndman, Rob J.; Booth, Heather; Yasmeen, Farah

doi:10.1007/s13524-012-0145-5

Cited by 234 publications

(278 citation statements)

References 30 publications

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“…Hyndman et al [13] extended the independent Hyndman and Ullah [7] functional time series model to model two quantities, the product function p x,t and the ratio function r x,t,i , where:…”

Section: Product-ratio Functional Modelmentioning

confidence: 99%

“…, I refers to the i-th sub-population and the f x,t,i are the smoothed age and time-specific mortality rates for the i-th population. As a smoothing procedure, Hyndman et al [13] use weighted penalized regression splines. A monotonic increasing constraint over time is imposed on ages x and above.…”

Section: Product-ratio Functional Modelmentioning

confidence: 99%

“…The weighting technique is used to cater for change over time in β x,l and b x,i,ξ . In contrast to Li and Lee [11], who used only the first principal component, the work of [13] used up to six components. The time-component for the product function K t,l displays a linear decreasing trend and therefore is more appropriately forecast using a non-stationary series model, ARI MA(p,d,q).…”

Section: Product-ratio Functional Modelmentioning

confidence: 99%

“…The Lee-Carter model and its earlier extensions are independent models [11] and, as such, forecast sub-populations (such as males and females) separately, failing to account for any relationship between groups [12]. Such independent models may produce divergent forecasts between two or more sub-populations, which may poorly represent the smaller populations within the same larger region or country [13].…”

Section: Introductionmentioning

confidence: 99%

“…Coherent models were developed to forecast the mortality of two or more sub-populations simultaneously and to ensure long-term non-divergent forecasts of sub-populations [11][12][13]. This type ∑ j=1 b x,i,j k t,i,j + x,t,i…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Extensions to Coherent Mortality Forecasting Models

Shair¹,

Purcal²,

Parr³

2017

Risks

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Coherent models were developed recently to forecast the mortality of two or more sub-populations simultaneously and to ensure long-term non-divergent mortality forecasts of sub-populations. This paper evaluates the forecast accuracy of two recently-published coherent mortality models, the Poisson common factor and the product-ratio functional models. These models are compared to each other and the corresponding independent models, as well as the original Lee-Carter model. All models are applied to age-gender-specific mortality data for Australia and Malaysia and age-gender-ethnicity-specific data for Malaysia. The out-of-sample forecast error of log death rates, male-to-female death rate ratios and life expectancy at birth from each model are compared and examined across groups. The results show that, in terms of overall accuracy, the forecasts of both coherent models are consistently more accurate than those of the independent models for Australia and for Malaysia, but the relative performance differs by forecast horizon. Although the product-ratio functional model outperforms the Poisson common factor model for Australia, the Poisson common factor is more accurate for Malaysia. For the ethnic groups application, ethnic-coherence gives better results than gender-coherence. The results provide evidence that coherent models are preferable to independent models for forecasting sub-populations' mortality.

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“…Hyndman et al [13] extended the independent Hyndman and Ullah [7] functional time series model to model two quantities, the product function p x,t and the ratio function r x,t,i , where:…”

Section: Product-ratio Functional Modelmentioning

confidence: 99%

Section: Product-ratio Functional Modelmentioning

confidence: 99%

Section: Product-ratio Functional Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluating Extensions to Coherent Mortality Forecasting Models

Shair¹,

Purcal²,

Parr³

2017

Risks

View full text Add to dashboard Cite

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Forecasting mortality rate by multivariate singular spectrum analysis

Mahmoudvand

Konstantinides

Rodrigues

2017

Appl Stoch Models Bus & Ind

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In this paper, we investigate the possibility of using multivariate singular spectrum analysis (SSA), a nonparametric technique in the field of time series analysis, for mortality forecasting. We consider a real data application with 9 European countries: Belgium, Denmark, Finland, France, Italy, Netherlands, Norway, Sweden, and Switzerland, over a period 1900 to 2009, and a simulation study based on the data set. The results show the superiority of multivariate SSA in comparison with the univariate SSA, in terms of forecasting accuracy.

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Projecting the future burden of cancer: Bayesian age–period–cohort analysis with integrated nested Laplace approximations

Riebler¹,

2017

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The projection of age-stratified cancer incidence and mortality rates is of great interest due to demographic changes, but also therapeutical and diagnostic developments. Bayesian age-period-cohort (APC) models are well suited for the analysis of such data, but are not yet used in routine practice of epidemiologists. Reasons may include that Bayesian APC models have been criticized to produce too wide prediction intervals. Furthermore, the fitting of Bayesian APC models is usually done using Markov chain Monte Carlo (MCMC), which introduces complex convergence concerns and may be subject to additional technical problems. In this paper we address both concerns, developing efficient MCMC-free software for routine use in epidemiological applications. We apply Bayesian APC models to annual lung cancer data for females in five different countries, previously analyzed in the literature. To assess the predictive quality, we omit the observations from the last 10 years and compare the projections with the actual observed data based on the absolute error and the continuous ranked probability score. Further, we assess calibration of the one-step-ahead predictive distributions. In our application, the probabilistic forecasts obtained by the Bayesian APC model are well calibrated and not too wide. A comparison to projections obtained by a generalized Lee-Carter model is also given. The methodology is implemented in the user-friendly R-package BAPC using integrated nested Laplace approximations.

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Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models

Cited by 234 publications

References 30 publications

Evaluating Extensions to Coherent Mortality Forecasting Models

Evaluating Extensions to Coherent Mortality Forecasting Models

Forecasting mortality rate by multivariate singular spectrum analysis

Projecting the future burden of cancer: Bayesian age–period–cohort analysis with integrated nested Laplace approximations

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