递推关系式的建立和应用

通过建立递进关系 ,求解数学中有关数量的问题     

Missing data and forecasting in multivariate time series: An application of the common components dynamic linear model

Summary The paper deals with missing data and forecasting problems in multivariate time series making use of the Common Components Dynamic Linear Model (DLMCC), presented in Quintana (1985), and West and Harrison (1989). Some results are presented and discussed: exploiting the correlation between series, estimated by the DLMCC, the paper shows as it is possible to update state vector posterior distributions for the unobserved series. This is realized on the base of the updating of the observed series state vectors, for which the usual Kalman filter equations can be applied. An application concerning some Italian private consumption series provides an example of the model capabilities.     

Economic Determinants of Net International Migration in Western Europe

This paper aims to estimate theinfluence of economic determinants on netinternational migration in Western Europe inthe period 1960–1998. Net migration rates(i.e., population growth minus natural increase,divided by the midyear population) constitutethe dependent variable. The economicdeterminants used in this study are GDP percapita, unemployment, and average educationallevel (amount of human capital) of thepopulation. Time series regression models havebeen used in country-specific analyses. Inaddition, a pooled cross-sectional time seriesanalysis has been made. The analyses suggestthat GDP per capita has a positive effect andunemployment a negative effect on a country'snet international migration.     

Approximation of density functions by orthogonal series with grouped data

Summary Letg(x) andf(x) be continuous density function on (a, b) and let {ϕ_j} be a complete orthonormal sequence of functions onL ₂(g), which is the set of squared integrable functions weighted byg on (a, b). Suppose that over (a, b). Given a grouped sample of sizen fromf(x), the paper investigates the asymptotic properties of the restricted maximum likelihood estimator of density, obtained by setting all but the firstm of the ϑ_j’s equal to0. Practical suggestions are given for performing estimation via the use of Fourier and Legendre polynomial series. Research partially supported by: CNR grant, n. 93. 00837. CT10.     

A hybrid ARIMA and support vector machines model in stock price forecasting

Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising.     

基于小波分析的石油价格长期趋势预测方法及其实证研究

本文将小波方法引入到油价长期趋势的预测中,利用小波多尺度分析的功能,提出了一种可以较为准确地根据油价时序列预测其未来长期走势的方法。这种方法的优点在于可以准确地提取油价的长期趋势,从总体上把握油价的非线性波动特征,从而能够很好地利用油价时间序列的历史数据,开展对未来一段时期内的多步预测。实证研究中,对Brent油价开展了时间跨度为1年的趋势预测,并将预测结果与ARIMA、GARCH、Holtwinters等方法得到的结果进行了比较,表明了基于小波分析的长期趋势预测法的预测能力是其他方法所不能比拟的,反映了本文所建立的石油价格长期趋势预测方法的有效性。     

仿射模型、广义仿射模型与上交所利率期限结构

本文以上交所利率期限结构从1996年6月至2003年2月的周样本数据作为分析对象,发现样本期内利率期限结构的平均形状是上斜的,而短期利率的基本趋势是下降的,说明长期债券具有较高的风险金。然后利用卡尔曼滤波法,实证分析了连续时间的两因子仿射、广义高斯仿射利率模型。结果表明模型下的利率期限结构与实际观测到的利率期限结构形状基本相同,说明模型能够反映利率期限结构的横截面特征。但两个模型对1,2,3,5,7年期利率的预测误差表现出一定序列相关性,说明模型还不能够很好地描述利率期限结构的时间序列特征。     

存在风险容差约束的模糊投资规划研究

在模糊不确定环境下,利用证券价格为梯形模糊数的模糊AR时间序列预测证券价格,描述市场运动趋势,将半绝对偏差风险约束调整为模糊松弛约束,在均值-半绝对偏差框架下,构建出目标函数服从梯形模糊数的可能性分布,风险约束为模糊松弛约束的模糊投资规划,并求得了有效性前沿。采用上证50的15只指标股进行实证检验,表明:规划可以给投资者带来较高的投资满意度水平;规划考虑了市场趋势,具有决策的针对性;风险的容差水平体现了投资者自身评定程度,在不同的市场行情下,风险容差水平具有不同的作用;规划比均值-半绝对偏差模型具有更高的有效性前沿,更加具有投资的针对性。     

The Value of Weather Information for E‐Commerce Operations

To be efficient, logistics operations in e‐commerce require warehousing and transportation resources to be aligned with sales. Customer orders must be fulfilled with short lead times to ensure high customer satisfaction, and the costly under‐utilization of workers must be avoided. To approach this ideal, forecasting order quantities with high accuracy is essential. Many drivers of online sales, including seasonality, special promotions and public holidays, are well known, and they have been frequently incorporated into forecasting approaches. However, the impact of weather on e‐commerce operations has not been rigorously analyzed. In this study, we integrate weather data into the sales forecasting of the largest European online fashion retailer. We find that sunshine, temperature, and rain have a significant impact on daily sales, particularly in the summer, on weekends, and on days with extreme weather. Using weather forecasts, we have significantly improved sales forecast accuracy. We find that including weather data in the sales forecast model can lead to fewer sales forecast errors, reducing them by, on average, 8.6% to 12.2% and up to 50.6% on summer weekends. In turn, the improvement in sales forecast accuracy has a measurable impact on logistics and warehousing operations. We quantify the value of incorporating weather forecasts in the planning process for the order fulfillment center workforce and show how their incorporation can be leveraged to reduce costs and increase performance. With a perfect information planning scenario, excess costs can be reduced by 11.6% compared with the cost reduction attainable with a baseline model that ignores weather information in workforce planning.     

实物资金流量表的预测方法研究

资金流量表是国民经济核算体系中的重要组成部分。然而,由于在编制过程中需要采集大量的数据,通常情况下,很多国家的资金流量表都会有较长时间的滞后。在编制实物资金流量表的延长表时,已有方法通常是基于基期与预测期交易收支结构保持不变的假定条件。然而,经济结构发生显著变化时,该类方法就会失效。基于上述问题,研究弱化模型的假设条件,并提出了新的实物资金流量表预测方法( 简称 FPTF方法)。根据表中元素必须满足的约束条件,该方法通过建立数学模型解除约束,其次基于历史数据的动态趋势,采用适当的时间序列分析方法来预测目标年份的实物资金流量表。通过仿真分析,验证了所提方法的有效性和稳定性。此外,基于中国1992年～2014 年的实物资金流量表数据进行实例分析,取得了满意的分析结果。