Mortality forecasting using a Lexis-based state-space model

IF 1.5 Q3 BUSINESS, FINANCE
Patrik Andersson, M. Lindholm
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引用次数: 2

Abstract

Abstract A new method of forecasting mortality is introduced. The method is based on the continuous-time dynamics of the Lexis diagram, which given weak assumptions implies that the death count data are Poisson distributed. The underlying mortality rates are modelled with a hidden Markov model (HMM) which enables a fully likelihood-based inference. Likelihood inference is done by particle filter methods, which avoids approximating assumptions and also suggests natural model validation measures. The proposed model class contains as special cases many previous models with the important difference that the HMM methods make it possible to estimate the model efficiently. Another difference is that the population and latent variable variability can be explicitly modelled and estimated. Numerical examples show that the model performs well and that inefficient estimation methods can severely affect forecasts.
使用基于Lexis的状态空间模型进行死亡率预测
摘要介绍了一种预测死亡率的新方法。该方法基于Lexis图的连续时间动力学,它给出的弱假设意味着死亡计数数据是泊松分布的。潜在死亡率采用隐马尔可夫模型(HMM)建模,该模型可实现完全基于似然的推断。通过粒子滤波方法进行似然推断,避免了近似假设,并提出了自然的模型验证方法。所提出的模型类包含了许多以前的模型作为特殊情况,其重要区别在于HMM方法可以有效地估计模型。另一个不同之处在于,总体和潜在变量可变性可以被明确地建模和估计。数值算例表明,该模型性能良好,但低效的估计方法会严重影响预测结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.10
自引率
5.90%
发文量
22
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