Lee–Carter mortality forecasting: a parallel generalized linear modelling approach for England and Wales mortality projections

Summary. The paper presents a reinterpretation of the model underpinning the Lee–Carter methodology for forecasting mortality (and other vital) rates. A parallel methodology based on generalized linear modelling is introduced. The use of residual plots is proposed for both methods to aid the assessment of the goodness of fit. The two methods are compared in terms of structure and assumptions. They are then compared through an analysis of the gender- and age-specific mortality rates for England and Wales over the period 1950–1998 and through a consideration of the forecasts generated by the two methods. The paper also compares different approaches to the forecasting of life expectancy and considers the effectiveness of the Coale–Guo method for extrapolating mortality rates to the oldest ages.     

考虑促销因素的医药电商平台需求预测研究

医药电商平台需求预测涉及到药品自身属性及电商平台推出的各种促销活动,本文针对以上影响药品销量的因素提出了时间序列-机器学习组合模型对医药电商平台进行需求预测。传统研究促销因素的需求预测文献将促销阶段商品销量拆分为常规销量和促销增量的线性组合,本文首先拟合各药品促销阶段的常规销量,根据各药品常规销量时间序列数据及服用周期,使用SARIMA模型拟合药品的常规销量预测值,并将常规销量预测值与商品促销特征数据一同输入XGBoost模型进行集成学习预测。本文使用国内某医药电商平台真实销售数据测试组合模型的有效性,结果显示组合预测模型的预测效果相比其他三种传统预测模型更优。此外,本文验证了不同折扣力度下组合预测模型的有效性,以及促销变量在预测模型中的有效性,同时研究了数据共享策略在需求预测中的应用场景,结果显示预测模型在引入促销变量和采用数据共享策略后都能显著降低模型的预测误差。     

Harrison–Stevens Forecasting and the Multiprocess Dynamic Linear Model

This is an expository article. The Harrison–Stevens forecasting algorithm using the multiprocess dynamic linear model is a robust method for forecasting in a nonstationary time series. The purpose of this article is to help statisticians become familiar with the method.     

智能组合预测方法及其应用

由于具有能以任意精度逼近任意复杂非线性函数的优良性能,神经网络在灰色系统预测中得到了较大的应用。在已有的研究基础上,针对灰色神经网络进化时容易陷入局部最优,参数修正受阻的问题,建立基于遗传粒子群混合算法优化的新型灰色神经网络模型。首先将灰色神经网络进行数学建模,以便于优化算法的应用;其次,综合遗传算法与粒子群算法的优点,构造一种混合算法,运用混合算法对灰色神经网络进行优化;最后通过日本入华游客数量预测的算例研究,比较新型灰色神经网络与灰色神经网络、单一算法优化的灰色神经网络的预测精度。所得结果表明,混合算法优化的新灰色神经网络具有更好的预测性能,在社会经济领域有着广泛的应用前景。     

On Using Hellinger Distance in Checking the Validity of Dynamic Approximations

The purpose of this article is to provide validation for the approximate algebraic propagation algorithms to accommodate non-Gaussian dynamic processes. These algorithms have been developed to carry out Bayesian analysis based on conjugate forms and presented with detailed examples of response distributions such as Poisson and Lognormal. The validity of the approximation algorithms can be checked by introducing a metric (Hellinger divergence measure) over the distribution of the states (parameters) and use it to judge the approximation. Theoretical bounds for the efficacy of such procedure are discussed.     

A note on the distribution of mean square error of forecasting

Simulation has been a very important and widely used method in the study of misspecification or order determination in time series analysis. Mean square error of forecasting (MSEF) has been a major criterion for comparing the performance of different models. In simulation studies, standard deviations of MSEF's are calculated from the computed values of the MSEF's, In this note, the distribution of MSEF from simulation studies is established. Exact variance of the MSEF can be obtained from the prespecified values of the model selected for simulation. This variance should be a more appropriate criterion for evaluating the performance between models.     

Forecasting the Distribution of Economic Variables in a Data-Rich Environment

This article investigates the relevance of considering a large number of macroeconomic indicators to forecast the complete distribution of a variable. The baseline time series model is a semiparametric specification based on the quantile autoregressive (QAR) model that assumes that the quantiles depend on the lagged values of the variable. We then augment the time series model with macroeconomic information from a large dataset by including principal components or a subset of variables selected by LASSO. We forecast the distribution of the h-month growth rate for four economic variables from 1975 to 2011 and evaluate the forecast accuracy relative to a stochastic volatility model using the quantile score. The results for the output and employment measures indicate that the multivariate models outperform the time series forecasts, in particular at long horizons and in tails of the distribution, while for the inflation variables the improved performance occurs mostly at the 6-month horizon. We also illustrate the practical relevance of predicting the distribution by considering forecasts at three dates during the last recession.     

基于灰色局势法改进的建筑工程投标决策方法研究

运用信息熵确定指标权重的方法,对灰色决策的基本原理进行了研究,建立了相应的数学模型。将非等权目标灰色局势决策方法用于工程投标决策,初步涉及了工程投标中存在的诸多不确定因素和风险性。从决定工程投标的影响因素入手,确定了灰色局势决策目标,将决策树和标准离差率（风险值）的计算结果作为量化了的决策目标,使灰色局势决策方法的基础数据来源更具有理论依据。结合工程实例,具体阐述了投标决策过程,对决策结果给予了细致分析,为工程施工企业做出正确的投标决策提供了依据。     

论“干中学”教学模式和方法——以市场调查与预测课程教学为例

结合三年来讲授市场调查与预测课程的经历。总结并提出了“干中学”教学模式和方法。其内容包括“干中学”教学模式的主要思路和方法,三年来市场调查与预测课中学生“干中学”的主要情况统计,取得的成绩和不足,对“干中学”教学模式的进一步思考。     

道德强度在灰色营销行为决策中的影响作用

以营销道德理论和灰色营销理论为基础,从卖方视角实证检验了道德强度(感知的后果严重性)对灰色营销道德决策的影响作用及其在决策过程的权变作用。结果表明:道德强度对道德判断和灰色营销行为倾向有直接的影响作用,会调节道德判断及惩罚预期和灰色营销行为倾向之间的负相关关系,但是对灰色营销的同情和灰色营销行为倾向之间的正相关关系没有调节作用。研究结果对于企业提升销售人员的道德判断水平,降低个人情感因素对决策者灰色营销行为的影响等具有实践价值。