Prediction of mortality rates in the presence of missing values

Tan, Chee Fai; Pooi, Ah Hin

doi:10.1063/1.4937109

Cited by 1 publication

(4 citation statements)

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“…In this section, we will incorporate time-varying stochastic parameters into the methodology by Pooi et al [6]. In [6], [8] and [9], the training data consisted of mortality rates from the years 1933 till 2000 (68 years) while the testing data consisted of mortality rates from the years 2001 till 2010 (10 years). Intuitively, among the data in the full training dataset, the earlier death rates may be less informative than the recent death rates in reflecting the "future" mortality range in the testing dataset.…”

Section: Prediction Of Us Mortality Rates Using a Model With Stochastmentioning

confidence: 99%

“…In general, the resulting prediction intervals in [6], [8] and [9] were found to have good ability in covering the observed future death rates. Although the prediction interval lengths may become longer for long-run prediction in [9], the prediction intervals in [8] performed well as they tend to have shorter interval lengths.…”

Section: Introductionmentioning

confidence: 99%

“…The US mortality data from the years 1933 till 2000 was fitted with a MPN distribution, which then is used to obtain the prediction intervals for future death rates of the years 2001 till 2010. Subsequently, this methodology was also applied in cases where the data used was augmented to include female death rates, death rates of Canada and UK [8] as well as applied on incomplete US mortality training dataset that contains missing values [9]. In general, the resulting prediction intervals in [6], [8] and [9] were found to have good ability in covering the observed future death rates.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, this methodology was also applied in cases where the data used was augmented to include female death rates, death rates of Canada and UK [8] as well as applied on incomplete US mortality training dataset that contains missing values [9]. In general, the resulting prediction intervals in [6], [8] and [9] were found to have good ability in covering the observed future death rates. Although the prediction interval lengths may become longer for long-run prediction in [9], the prediction intervals in [8] performed well as they tend to have shorter interval lengths.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of mortality rates using a model with stochastic parameters

Tan¹,

Pooi²

2016

AIP Conference Proceedings

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Prediction of future mortality rates is crucial to insurance companies because they face longevity risks while providing retirement benefits to a population whose life expectancy is increasing. In the past literature, a time series model based on multivariate power-normal distribution has been applied on mortality data from the United States for the years 1933 till 2000 to forecast the future mortality rates for the years 2001 till 2010. In this paper, a more dynamic approach based on the multivariate time series will be proposed where the model uses stochastic parameters that vary with time. The resulting prediction intervals obtained using the model with stochastic parameters perform better because apart from having good ability in covering the observed future mortality rates, they also tend to have distinctly shorter interval lengths.

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Section: Prediction Of Us Mortality Rates Using a Model With Stochastmentioning

confidence: 99%