基于分位自回归的中国人口死亡率动态预测

采用1994-2012年国家统计局公布的死亡率数据,针对Lee-Carter模型的预测方法提出改进,将分位自回归方法内置到Lee-Carter模型框架中,构建中国人口死亡率的分位自回归预测模型,对未来人口死亡率的变动趋势进行预测。通过与传统的均值回归方法比较,得出结论为:人口死亡率的分位自回归预测方法能够获取更加全面的信息,能够有效应对人类预期寿命被低估的可能,从而对未来死亡率的预测更加合理、可信。     

基于均衡关系的中国人口死亡率预测模型

人口死亡率反映了人口的死亡程度,准确预测死亡率是人口科学及人口经济学研究的重点之一,同时也是长寿风险测量的重要数据基础。基于Lee-Carter模型,探索中国大陆与台湾地区死亡率的相关性,通过协整分析考虑两地死亡率的长期均衡关系,创新性地建立基于相关性的向量误差修正模型(VECM),克服传统自回归移动平均模型(ARIMA)使用有限数据进行预测的局限性;均方预测误差作为检验标准,结果表明:基于VECM模型的预测效果比传统的预测效果更佳;基于中国大陆地区和台湾地区的死亡率长期均衡关系,可以为两地联合长寿债券的定价提供重要参考。     

我国男女两性老龄人口死亡率联合建模与预测

老龄人口死亡率建模和预测是长寿风险度量和养老金风险管理的基础。在我国,退休年龄及以上老龄人口死亡数据稀少,随机波动大,构建能够捕捉老龄人口死亡率随性别、年龄和时间变动的动态预测模型成为难题。本文采用Logistic两人口死亡率模型研究我国老龄人口死亡率的建模与预测。首先,运用死亡率数据质量较好的我国台湾地区数据,对模型结构进行选择,并检验模型的稳健性和预测性能。其次,基于我国大陆地区死亡率数据对模型结构进行二次验证和选择,应用所选模型对大 陆地区老龄死亡率进行建模和预测。结果显示,对于我国男女老龄死亡率的拟合和预测,Logistic 两人口模型均优于单人口CBD模型。最后,运用Logistic两人口死亡率模型对死亡率在年龄和时间两个维度上外推和预测,计算出时期和队列老龄人口分年龄的预期余寿,为养老金精算评估和长寿风险分析提供更准确的数据支持。     

中国高龄人口死亡率随机波动趋势分析

本文采用1996-2010年国家统计局公布的死亡率数据,以70岁男性人口作为高龄人口的代表,基于中国人口死亡率数据较少的特点,突破了传统Lee-Carter模型的框架,直接从死亡率改善产生的原因入手,采取Monte Carlo方法建立中国高龄人口死亡率随机波动趋势模型。通过对不同死亡率改善原因进行组合,从中选取最优模型来探究死亡率的随机趋势性与波动性的关系,更好的克服了死亡率普遍被低估的事实,使得对未来死亡率的预测更加准确与稳妥。     

考虑死亡率组内差异的Lee-Carter模型的改进与应用

文章在经典Lee-Carter模型的基础上,将各个时间、年龄组内的死亡率差异考虑入模型的构建中,提出死亡人口服从负二项分布的Lee-Carter模型改进形式,并运用中国1993～2009年分年龄分性别的死亡率对模型进行了量化分析。分析表明,改进后的模型优于经典泊松分布假设下的模型。模型残差图显示中国人口死亡现象没有队列效应。最后,本文运用改进后模型预测出未来6年内中国分性别分年龄的死亡率。     

加入出生年效应的死亡率预测及其在年金系数估计中的应用

人口死亡率下降与寿命的延长已经成为全球性的趋势。运用基于出生年效应的Lee-Carter模型对中国男性人口死亡率数据进行拟合,通过模型残差比较,发现该模型拟合效果更优。根据模型预测,发现中国男性人口的预期寿命随时间逐渐增加,但增加的幅度逐渐减少。将死亡率预测结果用于养老年金系数的估计,发现中国现行城镇职工养老保险个人账户的年金系数被严重低估,这将在未来给基本养老保险个人帐户带来很大的偿付压力。     

函数型死亡率预测模型

人口死亡率反映人口的死亡水平,是人口规模的重要影响因素,同时也是人寿保险精算的重要数据基础。从数据特征来看,死亡率作为年龄的函数,是一种典型的函数型数据。本文使用函数型数据方法分析中国人口数据,基于1994—2010年中国人口分年龄死亡数据,建立函数型死亡率预测模型,对未来分年龄死亡率进行预测,并通过生命表方法计算了未来平均预期寿命。同时通过对历史数据的预测,说明模型预测结果比较可信。     

Lee-Carter模型外推预测死亡率及偏差纠正

Lee-Carter模型是人口死亡率预测的常用模型,泊松最大似然估计法是该模型参数估计广为采纳的方法,模型中与时间相关的因子可建立时间序列模型并进行外推,进而实现死亡率的预测.由于时间因子与死亡率之间的非线性性,简单的外推会带来死亡率预测的低估偏差.这个偏差可以通过对数正态分布的性质进行纠正或者随机模拟方法进行无偏预测.     

死亡率预测模型的新进展

近半个世纪以来,世界范围内,人口死亡率整体上呈现下降趋势。而依据传统死亡模型对死亡率的预测往往高于实际水平,这给养老金财务安排和养老年金的成本核算带来了严重的不利影响。本文沿着死亡率模型的发展轨迹,回顾和总结了各类死亡率预测模型,对死亡率预测模型的最新进展做出评述,并对中国死亡率预测模型的选取给出了建议。     

我国人口死亡风险异质与混合模型研究

多人口随机死亡率模型是人口统计和保险精算领域的前沿问题。建立符合我国人口特征的死亡率预测模型，对人口预测、长寿风险度量和积极管理长寿风险具有重要意义。然而，当前研究中对人口死亡风险异质性问题的关注较少，不能为多人口随机死亡率建模提供科学的研究假设。本文从我国人口死亡风险异质性的检验出发，构建两性别人口死亡率联合预测的混合泊松公因子模型，并给出极大似然参数估计的迭代算法，对我国男女两性别人口死亡率进行联合建模和预测，最后用于对保险公司养老年金的长寿风险资本需求测算。研究表明，混合泊松公因子模型能够有效刻画人口死亡风险的异质性，提升模型拟合优度，有效避免传统模型低估人口死亡率改善的弊端，并且死亡率性别比变动趋势符合人类生物规律。在风险导向的第二代偿付能力体系下，本文提出的死亡率模型能够为保险公司提供更稳健的长寿风险资本评估。     

Modelling and forecasting mortality distributions in England and Wales using the Lee-Carter model

Lee and Carter proposed in 1992 a non-linear model m_xt = exp (a_x + b_x k_t + ε_xt) for fitting and forecasting age-specific mortality rates at age x and time t. For the model parameter estimation, they employed the singular value decomposition method to find a least squares solution. However, the singular value decomposition algorithm does not provide the standard errors of estimated parameters, making it impossible to assess the accuracy of model parameters. This article describes the Lee-Carter model and the technical procedures to fit and extrapolate this model. To estimate the precision of the parameter estimates of the Lee-Carter model, we propose a binomial framework, whose parameter point estimates can be obtained by the maximum likelihood approach and interval estimates by a bootstrap approach. This model is used to fit mortality data in England and Wales from 1951 to 1990 and to forecast mortality change from 1991 to 2020. The Lee-Carter model fits these mortality data very well with R² being 0.9980. The estimated overall age pattern of mortality a_x is very robust whereas there is considerable uncertainty in b_x (changes in the age pattern over time) and k_t (overall change in mortality). The fitted log age-specific mortality rates have been declining linearly from 1951 to 1990 at different paces and the projected rates will continue to decline in such a way in the 30 years prediction period.     

A comparison of models for dynamic life tables. Application to mortality data from the Valencia Region (Spain)

The graduation of mortality data aims to estimate the probabilities of death at age x, q ( x ), by means of an age-dependent function, whose parameters are adjusted from the crude probabilities that are directly obtainable from the data. However, current life tables have a problem, the need for periodic updates due to changes in mortality over short periods of time. The table containing mortality rates for different ages in different years, q ( xt ), is called a dynamic life table, which captures mortality variation over time. This paper proposes a review of the most commonly used dynamic models and compares the results obtained by each of them when applied to mortality data from the Valencia Region (Spain). The result of the comparison leads us to the conclusion that the Lee-Carter method offers the best results for both sexes, while that based on Heligman and Pollard functions provides the best fit for men alone. Our working method is of additional interest as it may be applied to mortality data for a wide range of ages in any geographical location, allowing the most appropriate dynamic life table to be selected for the case at hand.     

Mortality,longevity and experiments with the Lee–Carter model

The paper reviews the Lee-Carter modelling framework, illustrated with an application, and then extends the framework through the development of a wider class of generalised, parametric, non-linear models. The choice of error distribution is also generalised. These extensions permit the modelling and extrapolation of age-specific cohort effects as well as the more familiar age-specific period effects: the age-period-cohort version of the model is discussed with a worked example. The paper also provides a comparative study of simulation strategies for assessing risk in mortality rate predictions and the associated forecast estimates of life expectancy and annuity values in both period and cohort perspectives.     

三阶段DEA模型管理无效率估计注记

由于能够同时调整外部环境与随机误差等因素对效率计算的影响,由Fried et al.(2002)提出的三阶段DEA模型得到了广泛应用。但在对其中的管理无效率进行估计时,国内一些学者误用了Jondrow et al. (1982)的公式。本文给出了适用公式,并进一步给出了使用李双杰等(2007)提出的统一分布假设的三阶段DEA模型管理无效率的估计公式。     

A computationally efficient nonparametric approach for changepoint detection

In this paper we build on an approach proposed by Zou et al. (2014) for nonparametric changepoint detection. This approach defines the best segmentation for a data set as the one which minimises a penalised cost function, with the cost function defined in term of minus a non-parametric log-likelihood for data within each segment. Minimising this cost function is possible using dynamic programming, but their algorithm had a computational cost that is cubic in the length of the data set. To speed up computation, Zou et al. (2014) resorted to a screening procedure which means that the estimated segmentation is no longer guaranteed to be the global minimum of the cost function. We show that the screening procedure adversely affects the accuracy of the changepoint detection method, and show how a faster dynamic programming algorithm, pruned exact linear time (PELT) (Killick et al. 2012), can be used to find the optimal segmentation with a computational cost that can be close to linear in the amount of data. PELT requires a penalty to avoid under/over-fitting the model which can have a detrimental effect on the quality of the detected changepoints. To overcome this issue we use a relatively new method, changepoints over a range of penalties (Haynes et al. 2016), which finds all of the optimal segmentations for multiple penalty values over a continuous range. We apply our method to detect changes in heart-rate during physical activity.     

Hazard ratio inference in stratified clinical trials with time‐to‐event endpoints and limited sample size

The stratified Cox model is commonly used for stratified clinical trials with time‐to‐event endpoints. The estimated log hazard ratio is approximately a weighted average of corresponding stratum‐specific Cox model estimates using inverse‐variance weights; the latter are optimal only under the (often implausible) assumption of a constant hazard ratio across strata. Focusing on trials with limited sample sizes (50‐200 subjects per treatment), we propose an alternative approach in which stratum‐specific estimates are obtained using a refined generalized logrank (RGLR) approach and then combined using either sample size or minimum risk weights for overall inference. Our proposal extends the work of Mehrotra et al, to incorporate the RGLR statistic, which outperforms the Cox model in the setting of proportional hazards and small samples. This work also entails development of a remarkably accurate plug‐in formula for the variance of RGLR‐based estimated log hazard ratios. We demonstrate using simulations that our proposed two‐step RGLR analysis delivers notably better results through smaller estimation bias and mean squared error and larger power than the stratified Cox model analysis when there is a treatment‐by‐stratum interaction, with similar performance when there is no interaction. Additionally, our method controls the type I error rate while the stratified Cox model does not in small samples. We illustrate our method using data from a clinical trial comparing two treatments for colon cancer.     

Assessing the use of sample selection models in the estimation of fertility postponement effects

Several studies have shown that at the individual level there exists a negative relationship between age at first birth and completed fertility. Using twin data in order to control for unobserved heterogeneity as possible source of bias, Kohler et al. (2001) showed the significant presence of such "postponement effect" at the micro level. In this paper, we apply sample selection models, where selection is based on having or not having had a first birth at all, to estimate the impact of postponing first births on subsequent fertility for four European nations, three of which have now lowest-low fertility levels. We use data from a set of comparative surveys (Fertility and Family Surveys), and we apply sample selection models on the logarithm of total fertility and on the progression to the second birth. Our results show that postponement effects are only very slightly affected by sample selection biases, so that sample selection models do not improve significantly the results of standard regression techniques on selected samples. Our results confirm that the postponement effect is higher in countries with lowest-low fertility levels.