供应链环境下合作预测效果的分析

本文定量分析了供应链中合作预测对需求长鞭效应的减小作用,在多节点企业所组成的供应链中,合作预测对订单量的波动和需求预测误差的波动起到积极的抑制作用。本文通过理论研究和数值分析的结果表明,供应链信息共享和合作预测能够控制需求流动现象。     

论经济周期波动的随机统计规律

从经济周期波动的特征出发，分析经济周期现象中考虑随机性的合理性和必要性，可看出经济周期运动的内在机制中蕴含着大量的不确定随机因素及对经济周期波动的影响，从随机周期现象入手，针对周期波动的研究历史和现状，就可得到新的启示和评价．     

贵州省退耕还林地经济收益预测

退耕还林工程的可持续性,也就是该工程是否能达到预期的目标,关键要看农民是否能减弱或者摆脱对土地的依赖。而退耕还林地的未来收益将是退耕农户未来收入的组成部分,这将直接影响退耕还林后续政策的制定。因此,对退耕还林地未来收益的预测是后续政策研究的基础之一。该文在对贵州省进行实地调研的基础上,以马尾松林和核桃林分别作为生态林和经济林的代表,对其不同年份的净收益进行了预期,在此基础上,通过与退耕还林机会成本的比较,得出马尾松和核桃种植农户的收益情况。通过计算,得出对贵州省退耕还林的农户,至少是对还生态林的农户来说,现行补助年限过短的结论。     

基于重现时间间隔分析的极端收益预测及交易策略研究

极端收益的预测在金融风险管理中非常重要。本文系统研究了极端收益重现时间间隔的统计规律，提出了一种基于重现时间间隔分析的早期预警模型，并对极端收益的重现进行预测，检验了模型在样本内外的预测性能；最后分别针对极端正收益和极端负收益的样本外预测结果，设计了看涨和看跌的两种交易策略，并以中国上证指数、法国CAC40指数、英国富时指数、香港恒生指数和日本日经指数为例，对交易策略的日均收益率进行了统计显著性检验。研究结果表明，极端收益的重现时间间隔具有右偏、尖峰厚尾和强自相关等典型特征；极端收益预测模型在样本内和样本外检验中都具有良好的预测能力；看涨和看跌交易策略在卖出区间均能有效地避开下跌阶段，看涨策略有更显著的盈利水平。     

广义递归方差倒数组合预测方法研究

在递归等权组合预测方法（ＲＥＷ法）￣［１］和递归方差倒数组合预测方法（ＲＶＲＷ法）￣［２］的思路基础上，以方差的幂函数倒数构造组合权重，进一步提出了广义递归方差倒数组合预测方法（ＧｅｎｅｒａｌｉｚｅｄＲｅｃｕｒｓｉｖｅＶａｒｉａｎｃｅＲｅｃｉｐｒｏｃａｌＷｅｉｇｈｔｉｎｇ，即ＧＲＶＲＷ法），给出了有关的迭代计算方法。该方法更一般地体现了以预测精度作为组合权重依据的思想，将ＲＥＷ法和ＲＶＲＷ法概括为其特殊情况，实例表明，ＧＲＶＲＷ法可以明显地提高预测精度。     

宋元话本预测术与社会生活论略

对神秘预测术之狂热信仰,是宋元话本的一大特点。作品中之人物,小至衣食住行、送往迎来,大至家族兴衰、朝代荣枯、军国大事,都无不以之为指针。这一现象出现的社会根源一是此类信仰在民间的广泛流布;二是统治者的倾心膜拜,皇帝及上层官吏甚至还常常把它当成操纵政治军事运作的工具,作为官吏遴选任免之根据,甚至连皇权传承储君之决定也以此为准的。这些术数及极一世之盛的以预测为旨归的巫术鬼神信仰的负面影响,常常导致民财之耗蠹,吏治之变乱,民命之残害等。     

Real-Time Forecasting With a Mixed-Frequency VAR

This article develops a vector autoregression (VAR) for time series which are observed at mixed frequencies—quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to implement a data-driven hyperparameter selection. Using a real-time dataset, we evaluate forecasts from the mixed-frequency VAR and compare them to standard quarterly frequency VAR and to forecasts from MIDAS regressions. We document the extent to which information that becomes available within the quarter improves the forecasts in real time. This article has online supplementary materials.     

Tourist number prediction of historic buildings by singular spectrum analysis

A wooden historic building located in Tibet, China, experienced structural damage when subjected to tourists visit. This kind of ancient building attends to too many visitors every day because heritage sites never fail to attract tourists. There should be a balance between accepting the visitors and the protection of historic buildings considering the importance of the cultural relics. In this paper, the singular spectrum analysis (SSA) is used for forecasting the number of tourist for the building management to exercise maintenance measures to the structure. The analyzed results can be used to control the tourist flow to avoid excessive pedestrian loading on the structure. The relationship between the measured acceleration from the structure and the tourist number is firstly studied. The root-mean-square (RMS) value of the measured acceleration in the passage route of the tourist is selected for forecasting future tourist number. The forecasting results from different methods are compared. The SSA is found slightly outperforms the autoregressive integrated moving average model (ARIMA), the X-11-ARIMA model and the cubic spline extrapolation in terms of the RMS error, mean absolute error and mean absolute percentage error for long-term prediction, whereas the opposite is observed for short-term forecasting.     

On the Optimal Use of Suboptimal Forecasts of Explanatory Variables

Ashley (1983) gave a simple condition for determining when a forecast of an explanatory variable (X_t ) is sufficiently inaccurate that direct replacement of X_t by the forecast yields worse forecasts of the dependent variable than does respecification of the equation to omit X_t . Many available macroeconomic forecasts were shown to be of limited usefulness in direct replacement. Direct replacement, however, is not optimal if the forecast's distribution is known. Here optimal linear forms in commercial forecasts of several macroeconomic variables are obtained by using estimates of their distributions. Although they are an improvement on the raw forecasts (direct replacement), these optimal forms are still too inaccurate to be useful in replacing the actual explanatory variables in forecasting models. The results strongly indicate that optimal forms involving several commercial forecasts will not be very useful either. Thus Ashley's (1983) sufficient condition retains its value in gauging the usefulness of a forecast of an explanatory variable in a forecasting model, even though it focuses on direct replacement.     

Bayesian Dynamic Factor Models and Portfolio Allocation

We discuss the development of dynamic factor models for multivariate financial time series, and the incorporation of stochastic volatility components for latent factor processes. Bayesian inference and computation is developed and explored in a study of the dynamic factor structure of daily spot exchange rates for a selection of international currencies. The models are direct generalizations of univariate stochastic volatility models and represent specific varieties of models recently discussed in the growing multivariate stochastic volatility literature. We discuss model fitting based on retrospective data and sequential analysis for forward filtering and short-term forecasting. Analyses are compared with results from the much simpler method of dynamic variance-matrix discounting that, for over a decade, has been a standard approach in applied financial econometrics. We study these models in analysis, forecasting, and sequential portfolio allocation for a selected set of international exchange-rate-return time series. Our goals are to understand a range of modeling questions arising in using these factor models and to explore empirical performance in portfolio construction relative to discount approaches. We report on our experiences and conclude with comments about the practical utility of structured factor models and on future potential model extensions.