英文标题：
《A unified approach to mortality modelling using state-space framework:
  characterisation, identification, estimation and forecasting》
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作者：
Man Chung Fung, Gareth W. Peters, Pavel V. Shevchenko
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最新提交年份：
2016
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英文摘要：
  This paper explores and develops alternative statistical representations and estimation approaches for dynamic mortality models. The framework we adopt is to reinterpret popular mortality models such as the Lee-Carter class of models in a general state-space modelling methodology, which allows modelling, estimation and forecasting of mortality under a unified framework. Furthermore, we propose an alternative class of model identification constraints which is more suited to statistical inference in filtering and parameter estimation settings based on maximization of the marginalized likelihood or in Bayesian inference. We then develop a novel class of Bayesian state-space models which incorporate apriori beliefs about the mortality model characteristics as well as for more flexible and appropriate assumptions relating to heteroscedasticity that present in observed mortality data. We show that multiple period and cohort effect can be cast under a state-space structure. To study long term mortality dynamics, we introduce stochastic volatility to the period effect. The estimation of the resulting stochastic volatility model of mortality is performed using a recent class of Monte Carlo procedure specifically designed for state and parameter estimation in Bayesian state-space models, known as the class of particle Markov chain Monte Carlo methods. We illustrate the framework we have developed using Danish male mortality data, and show that incorporating heteroscedasticity and stochastic volatility markedly improves model fit despite an increase of model complexity. Forecasting properties of the enhanced models are examined with long term and short term calibration periods on the reconstruction of life tables.
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中文摘要：
本文探索和发展了动态死亡率模型的替代统计表示和估计方法。我们采用的框架是在一般状态空间建模方法中重新解释流行的死亡率模型，如Lee-Carter类模型，它允许在统一框架下建模、估计和预测死亡率。此外，我们还提出了另一类模型识别约束，该约束更适用于基于边缘化似然最大化的过滤和参数估计设置中的统计推理或贝叶斯推理中的统计推理。然后，我们开发了一类新的贝叶斯状态空间模型，该模型结合了关于死亡率模型特征的先验信念，以及与观察到的死亡率数据中的异方差相关的更灵活和适当的假设。我们证明了在一个状态空间结构下可以投射多周期和队列效应。为了研究长期死亡率动态，我们在周期效应中引入随机波动性。使用最近一类专门为贝叶斯状态空间模型中的状态和参数估计而设计的蒙特卡罗程序（称为粒子马尔可夫链蒙特卡罗方法）对由此产生的随机波动率死亡率模型进行估计。我们用丹麦男性死亡率数据说明了我们开发的框架，并表明，尽管模型复杂度增加，但加入异方差和随机波动性显著改善了模型拟合。在生命表重建的长期和短期校准周期中，检验了增强模型的预测特性。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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