动态死亡率模型的研究进展

Reviews on the Research of Dynamic Mortality Models

死亡率的预测是人口预测的基础.近年来,死亡率建模方法不断取得新的进展,从最早的静态死亡率模型开始,死亡率模型不断发展为包括时间项的动态预测模型,如Lee-Carter族模型、CBD族模型等.本文对死亡率预测模型的相关文献进行了回顾和梳理.随着动态模型的发展,一些学者从死亡率改善水平入手,发展出一系列死亡率改善模型.另外,随着死亡率研究的深入,多人口死亡率的建模引起了研究者的重视,多人口预测模型迅速发展和完善.随着死亡率模型的研究方法不断丰富和创新,新兴统计学方法(如机器学习等)已经在死亡率建模中有所应用,拟合和预测准确度不断提升.除了经典的建模方法的扩展外,例如小区域人口或数据缺失的人口、高龄人口、相关人口等死亡率建模问题仍值得研究.

Mortality forecasting is the basis of population forecasting.In recent years,new progress has been made in mortality models.From the earliest static mortality models,mortality models have been developed into dynamic forecasting models including time terms,such as Lee-Carter model family,CBD model family and so on.This paper reviews and sorts out relevant literature on mortality forecasting models.With the development of dynamic models,some scholars have developed a series of mortality improvement models based on the level of mortality improvement.In addition,with the progress of mortality research,multi-population mortality modeling attracted the attention of researchers,and the multi-population forecasting models have been constantly developed and improved,which play an important role in the mortality forecasting.With the continuous enrichment and innovation of mortality model research methods,new statistical methods(such as machine learning) have been applied in mortality modeling,and the accuracy of fitting and prediction has been improved.In addition to the extension of classical modeling methods,issues such as small-area population or missing data of the population,the elderly population,the related population mortality modeling are still worth studying.