多维家庭人口预测模型的建立及应用

本文建立了多维的家庭人口预测模型。该模型可用于同时预测家庭户以及人口的规模、结构、特征及分布,并保证人口事件（如结婚、离婚、再婚子女离家等）的发生在男性人口与女性人口之间的一致性以及在父母辈与子女辈之间的一致性。与国外学者近年提出的多维宏观家庭户预测模型相比,本文建立的模型的一个明显优点是无需从专项家庭抽样调查得到的家庭户类型转换概率数据,只需通常的生育、死亡、婚姻等人口数据。与传统的户主率方法不同,本文模型将家庭户预测与未来生育、死亡、婚姻等人口事件发生率紧密地联系起来,可同时预测核心与三代家庭户,既可用于中国等发展中国家,又可用于西方发达国家     

中国随机人口预测

本文应用ＬＴＣ方法首次完成了中国随机人口预测，并全面介绍了这一方法。给出了从１９９０年到２０５０年的包括人口数、老龄比和负担比变化的期望和置信区间等主要结果，显示了这种预测的多种用途，如评价确定性方案预测的可能性等，弥补了传统的“高一低”方案法所无法回答发生某个状态概率的缺点，为科学决策服务。     

Lee-Carter死亡率模型的估计与应用——基于中国人口数据的分析

文章选择Lee-Carter死亡率模型对中国人口死亡率数据进行拟合和预测,以探讨Lee-Carter模型在中国的适用性和表现形式。基于1992～2007年中国人口分年龄组死亡率数据,文章对奇异值分解法(SVD)、最小二乘法(OLS)、加权最小二乘法(WLS)和极大似然法(MLE)的拟合结果和预测能力进行了比较分析。结果表明,加权最小二乘法具有最好的拟合和预测效果。文章利用最优的拟合模型,对未来中国人口平均预期寿命进行了预测,并利用Bootstrap方法进行了区间估计。     

健康期望寿命估算方法的拓展及其在中国高龄老人研究中的应用

文章运用一种新的估算方法估算了纠正偏差后的中国高龄老人日常生活自理能力完好和失能期望寿命(可称为健康与非健康期望寿命),首次对发展中国家高龄老人分年龄、性别与分生活自理能力的死亡率和健康的多状态转移概率进行分析。结果表明,健康与非健康期望寿命和起点年龄的生活自理能力状态有很大关系。将临终前生活自理能力和卧床不起天数数据相结合,文章还分析了高龄老人临终前痛苦程度在性别和年龄上的差异,并根据中国高龄老人的研究结果讨论评估了在人口老化进程中,老年残障期大扩展、相对减缩与保持均衡的三种理论假说。     

基于时间—区域序列的随机死亡率预测及对中国数据的应用

由美国学者Lee,Tulj(?)parkar和Carter于1991年提出的随机人口预测(LTC)方法,既弥补了传统的“高一低”方案所无法回答发生某个状态的概率的缺点,又解决了经典的随机人口模型不能处理按龄生育、死亡率的随机性时变的困难。介绍了(LTC)方法的主要内容,并通过将其中时间序列模型扩展为时间—区域序列模型,解决了该方法用于发展中国家的常见困难即死亡率时间序列数据不足的问题。同时利用中国死亡率数据进行(LTC)方法的建模和预测,取得了良好的效果。     

多维度失能测度指标体系构建及失能分级研究

以ICF理论框架的交互影响理念为依据,从多维度视角构建了失能测度指标体系。首先,通过赋值法,得到多维度下的失能总分,依据得分将其划分为重度失能、偏重度失能、中度失能、轻度失能及健康五种功能状态,构造死亡概率模型及失能状态转移概率模型,结果显示,失能分级标准是可靠的。其次,利用CLHLS数据库2011年及2014年追踪调查数据,通过构造多状态转移概率矩阵,实证测算了多状态失能转移概率;借助第六次全国人口普查数据,估算了中国65岁及以上失能人口规模、各状态维持时间及预期寿命等。结果显示,初始健康的老人三年期转移概率维持在健康的概率最高,其死亡率也最低;而初始状态为重度失能的老人死亡率最高,其维持重度失能及转移到偏重度等失能状态的概率也最高。研究结论表明,直接依据数据的失能状态转移概率测算比建模估计要准确;基于多状态转移概率矩阵评估的失能规模、预期寿命等均比已有分级测度结果可靠性更高。     

中国老年长期护理服务需求评估与预测

在人口老龄化的背景下,随着医疗和护理等服务成本不断攀升,加之家庭小型化、青年人口迁移等原因,老年护理成本带来的负担将不断加重,迫切需要对老年护理服务需求进行评估和预测。文章基于中国老年健康影响因素跟踪调查(CLHLS)纵贯数据,运用马尔科夫链方法估算老年健康状态转移概率;同时结合人口预测结果,估算和预测不同年份、不同失能状态老年人的数量;结合全国老年服务调查数据,估算和预测老年护理服务需求。预测结果显示,2014~2050年老年护理服务潜在需求可能从3 089.96亿元上升至4.27万亿元;有效需求可能从1 172.42亿元上升至1.60万亿元;重度失能老人护理服务有效需求可能从276亿元上升至4 944亿元,这是长期护理的重点人群。老年护理服务需求增速迅猛,服务供给体系和应对机制亟待建立。     

应用模糊数学综合判断人口年龄结构类型

人口统计中年龄结构类型的划分是根据反映年龄结构的统计指标按规定标准将人口总体分为不同的类型。由于传统方法确定人口类型常出现划分上的矛盾,以及无法描述同类型差异的情况,这里采用了模糊综合评判法。目前模糊综合评判法分为两类:单对象多指标多等级的评价和多对象多指标无等级的评价。通过对这两类方法的改进,兼容它们的优点,扬弃它们的缺点,既能描述等级程度和排序,又能一次性地实现多对象综合评价。     

关于我国农村剩余劳动力数量测算方法的研究述评

本文对关于我国农村剩余劳动力数量测算方法的研究进行了综述,根据各种方法的测算基础、测算思路及其特征,笔者将其分为六类：简单计算法、国际比较法、生产函数法、总量分解法、两部门（地区）法和其他方法（统计指标比对法、数理与计量经济模型法、综合法）等。在对各类方法进行了详细阐述及评价的基础上,笔者提出了自己的观点,即应当根据其特点,综合使用各类方法从不同角度测算我国农村剩余劳动力的数量。另外,笔者还认为国家统计部门应当适当考虑学术界的研究需要,我国农村剩余劳动力的年龄、教育等结构问题应当成为进一步研究的重点。     

人口预测的随机方法:基于Leslie矩阵和ARMA模型

本文探讨了人口预测的一种随机方法。文章回顾了经典的Leslie矩阵并结合中国的人口统计数据,用时间序列的ARMA模型对未来的生育率、死亡率进行估计,并由此构造Les-lie矩阵,经时间序列的数据中心化,根据自相关函数、偏自相关函数的截尾性或拖尾性,以及贝叶斯信息准则函数方法对模型定阶,实现对ARMA模型的识别。在中国人口预测方面的应用证明,基于Leslie矩阵和ARMA模型的人口随机预测方法是稳健的,具有很强的适用性。由于统计数据可获得性的局限,对模型做了不少假设和近似。随着人口数据的积累,未来将会在此方面有所改进。     

Probabilistic Forecasting Using Stochastic Diffusion Models, With Applications to Cohort Processes of Marriage and Fertility

In this article, we show how stochastic diffusion models can be used to forecast demographic cohort processes using the Hernes, Gompertz, and logistic models. Such models have been used deterministically in the past, but both behavioral theory and forecast utility are improved by introducing randomness and uncertainty into the standard differential equations governing population processes. Our approach is to add time-series stochasticity to linearized versions of each process. We derive both Monte Carlo and analytic methods for estimating forecast uncertainty. We apply our methods to several examples of marriage and fertility, extending them to simultaneous forecasting of multiple cohorts and to processes restricted by factors such as declining fecundity.     

Statistically tested comparisons of the accuracy of forecasting methods for age-specific and sex-specific mortality and life expectancy

Although there are continuing developments in the methods for forecasting mortality, there are few comparisons of the accuracy of the forecasts. The subject of the statistical validity of these comparisons, which is essential to demographic forecasting, has all but been ignored. We introduce Friedman's test statistics to examine whether the differences in point and interval forecast accuracies are statistically significant between methods. We introduce the Nemenyi test statistic to identify which methods give results that are statistically significantly different from others. Using sex-specific and age-specific data from 20 countries, we apply these two test statistics to examine the forecast accuracy obtained from several principal component methods, which can be categorized into coherent and non-coherent forecasting methods.     

Bayesian Probabilistic Projections of Life Expectancy for All Countries

We propose a Bayesian hierarchical model for producing probabilistic forecasts of male period life expectancy at birth for all the countries of the world to 2100. Such forecasts would be an input to the production of probabilistic population projections for all countries, which is currently being considered by the United Nations. To evaluate the method, we conducted an out-of-sample cross-validation experiment, fitting the model to the data from 1950–1995 and using the estimated model to forecast for the subsequent 10 years. The 10-year predictions had a mean absolute error of about 1 year, about 40 % less than the current UN methodology. The probabilistic forecasts were calibrated in the sense that, for example, the 80 % prediction intervals contained the truth about 80 % of the time. We illustrate our method with results from Madagascar (a typical country with steadily improving life expectancy), Latvia (a country that has had a mortality crisis), and Japan (a leading country). We also show aggregated results for South Asia, a region with eight countries. Free, publicly available R software packages called bayesLife and bayesDem are available to implement the method.     

对近年来中国人口统计学学科发展状况的评述

本文对2002年下半年以来中国人口数据和人口统计分析方法的状况进行描述、归纳和评述,其中包括对普查数据质量的分析、生育率分析、死亡率分析、出生性别比分析、流动人口分析、人口老龄化研究和人口预测及其方法研究,以及对学科发展态势的评价。     

European Demographic Forecasts Have Not Become More Accurate Over the Past 25 Years

Nowadays, demographers, population statisticians, and population forecasters have richer data, more refined theories of demographic behavior, and more sophisticated methods of analysis than they had two or three decades ago. This scientific progress should have made it easier to predict demographic behavior. But analyses of the errors in older forecasts show that demographic forecasts published by statistical agencies in 14 European countries have not become more accurate over the past 25 years. The findings demonstrate that scientific progress in population studies during the previous two to three decades has not kept up with the trend toward less predictable demographic behavior of populations in European countries. There is no reason to be more optimistic about US Census Bureau forecasts. Population forecasts are intrinsically uncertain, hence should be couched in probabilistic terms.     

Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods

Mortality rates are often disaggregated by different attributes, such as sex, state, education, religion, or ethnicity. Forecasting mortality rates at the national and sub-national levels plays an important role in making social policies associated with the national and sub-national levels. However, base forecasts at the sub-national levels may not add up to the forecasts at the national level. To address this issue, we consider the problem of reconciling mortality rate forecasts from the viewpoint of grouped time-series forecasting methods (Hyndman et al. in, Comput Stat Data Anal 55(9):2579–2589, 2011). A bottom-up method and an optimal combination method are applied to produce point forecasts of infant mortality rates that are aggregated appropriately across the different levels of a hierarchy. We extend these two methods by considering the reconciliation of interval forecasts through a bootstrap procedure. Using the regional infant mortality rates in Australia, we investigate the one-step-ahead to 20-step-ahead point and interval forecast accuracies among the independent and these two grouped time-series forecasting methods. The proposed methods are shown to be useful for reconciling point and interval forecasts of demographic rates at the national and sub-national levels, and would be beneficial for government policy decisions regarding the allocations of current and future resources at both the national and sub-national levels.     

The Level and Age Pattern of Mortality in Bandafassi (Eastern Senegal): Results From a Small-Scale and Intensive Multi-Round Survey

The data collected in the Bandafassi demographic study in Eastern Senegal, a small-scale intensive and experimental follow-up survey of a population of about 7,000 in 1983, were analysed to derive an estimated life table. The use of multi-round surveys, combined with anthropological methods to estimate ages and collect genealogies, has resulted in unusually reliable data. Taking into account the uncertainty of the estimates due to the small size of the population, mortality was high, with life-expectancy at birth close to 31 years; a pattern of infant and child mortality close to that observed in other rural areas of Senegal, with a very high level or mortality between ages six months and three years; a seasonal pattern in child mortality with two high-risk periods, the rainy season and the end of the dry season; an adult mortality pattern similar to that described in model life tables for developed countries; no significant difference by sex or ethnic group. The Bandafassi population study and a few similar studies suggest that one possible way to improve demographic estimates in countries where vital registration systems are defective would be to set up a sample of population laboratories where intensive methods of data collection would continue for extended periods.     

Population Scenarios Based on Probabilistic Projections: An Application for the Millennium Ecosystem Assessment

Probabilistic population forecasts offer a number of advantages to users. However, in some cases population is one component of a larger analysis that may take a different approach to uncertainty. For example, integrated assessments of environmental issues such as climate change or ecosystem degradation have typically used a small number of alternative scenarios to explore uncertainty in future environmental outcomes. In such cases, population projections that are provided only as probability distributions are difficult to use. I present a method of employing probabilistic population projections to derive individual, deterministic projections that can be used within scenarios for integrated assessments. The principal advantages of this approach are that (1) it provides a less ad hoc way of defining deterministic projections intended to be consistent with more comprehensive scenarios that describe, among other things, future socio-economic developments; (2) it provides more flexibility in specifying input assumptions for deterministic projections as compared to choosing off-the-shelf projections, allowing population assumptions to be tailored to the scenario; and (3) it provides a quantitative assessment of the uncertainty associated with any given deterministic projection. I describe the application of the method to the development of population projections used in integrated scenarios for the Millennium Ecosystem Assessment, an international scientific effort to assess the current conditions of and future outlook for global ecosystem goods and services. Results show that the MA scenarios are each consistent with a relatively wide range of demographic outcomes. For some scenarios, ranges of plausible outcomes in some regions overlap substantially, indicating that particular population projections could be consistent with more than one scenario. In other cases, uncertainty ranges for different scenarios are distinct, indicating that a projection consistent with one scenario is unlikely to be also consistent with another. Comparing variances of the conditional projections also provides insight into how much different storylines constrain future demographic developments. The development of the MA projections points to important areas of future research on correlations among demographic rates and on uncertainty across scales. It also serves as an illustration of how probabilistic and alternative scenario-based approaches to uncertainty can be combined within a single integrated analysis.This revised version was published online in April 2005 with corrections to figures 1-3.     

Future life expectancy in Australia,Europe, Japan and North America

Human life expectancy has risen in most developed countries over the last century, causing the observed demographic shifts. Babel, Bomsdorf and Schmidt (forthcoming) introduce a stochastic mortality model using panel data procedures which distinguishes between a common time effect and a common age effect of mortality evolvement. Using this mortality model, the present paper provides forecasts of future life expectancy for 17 countries divided into 12 regions: Australia, Alps, Bene, Canada, England and Wales, France, Germany, Italy, Japan, Spain, Scandinavia and the United States of America. We consider (traditional) period life expectancies as well as cohort life expectancies, the latter being a more realistic approach but less common. It turns out that a continuing increase of life expectancy is expected in all considered countries. Further, we show that the probabilistic uncertainty of forecast life expectancies is different if either period life expectancies or cohort life expectancies are considered and, moreover, the uncertainty increases substantially if the error of parameter estimation is included.     

MAPE-R: a rescaled measure of accuracy for cross-sectional subnational population forecasts

Accurately measuring a population and its attributes at past, present, and future points in time has been of great interest to demographers. Within discussions of forecast accuracy, demographers have often been criticized for their inaccurate prognostications of the future. Discussions of methods and data are usually at the centre of these criticisms, along with suggestions for providing an idea of forecast uncertainty. The measures used to evaluate the accuracy of forecasts also have received attention and while accuracy is not the only criterion advocated for evaluating demographic forecasts, it is generally acknowledged to be the most important. In this paper, we continue the discussion of measures of forecast accuracy by concentrating on a rescaled version of a measure that is arguably the one used most often in evaluating cross-sectional, subnational forecasts, Mean Absolute Percent Error (MAPE). The rescaled version, MAPE-R, has not had the benefit of a major empirical test, which is the central focus of this paper. We do this by comparing 10-year population forecasts for U.S. counties to 2000 census counts. We find that the MAPE-R offers a significantly more meaningful representation of average error than MAPE in the presence of substantial outlying errors, and we provide guidelines for its implementation.