有关DEA模型$(C^2GS^2)$的最优值的定理及其在预测方法简化中的应用

文献［１］中在将初始点选在生产可能集内的基础上用弱ＤＥＡ 有效性$(C^2GS^2)$进行预测,但有时难于在生产可能集内选取初始点,因此本文在给出一些有关ＤＥＡ 模型$(C^2GS^2)$的最优值的定理的基础上设法省略该步骤．对任给的有正输入与正输出的初始点,不必检验它是否在生产可能集内,只要ＤＥＡ 模型的最优值存在且为正,就可用弱ＤＥＡ 有效性$(C^2GS^2)$ 进行预测     

基于广义回归神经网络的交通事故预测

在进行交通事故统计的基础上,运用广义回归神经网络理论,建立交通事故预测模型,对2000~2003年的交通事故死亡人数和经济损失进行外推预测.计算结果表明,该模型预测和外推精度高,可用于交通事故预测.     

水环境容量法在城市人口规模预测中的应用初探——以福建省长汀县为例

回顾了现在城市规划中人口规模预测的几种常用方法,提出在注重生态环境和可持续发展的背景下,在城市总体规划人口规模的预测中,除考虑经济和社会发展诸因素外,还应根据各地的实际情况因地制宜,找出其主要的环境制约因素,运用生态平衡原理,推算合理的城市人口规模。本文把水环境容量和人口规模预测结合起来,进行了一种新的环境容量法人口规模预测的应用探索,即水环境容量法,并说明了本方法的适用范围。本方法充分考虑了水体的自净能力,以水环境容量为环境限制因子,预测了水环境容量阈值允许范围内的人口规模。     

浅谈消费税的税务筹划

阐述了有关消费税的一些概念,举例探讨了消费税的税务筹划.     

基于CDMA 1X VPDN的银联无线POS系统应用

CDMA 1X VPDN技术是一种新型通信技术,它利用CDMA 1X的无线接入,为企业客户提供了一种企业VPDN解决方案.文章介绍了CDMA 1X VPDN核心技术(包括隧道技术、VPDN的安全性协议等),阐述了该技术的结构与原理,分析了CDMA 1X网络无线侧安全性,最后详细介绍了银联无线POS VPDN的实际应用及其优点.     

枯季径流预报探讨

在分析研究流域气候特征及枯季径流来水规律的基础上,用水文方法分别建立了枯水期月径流预报模型。水文方法是利用水文序列资料建立自回归模型,用实测资料对预报模型进行了验证。     

模拟和数字电视覆盖区方法的计算与分析

本论文通过对几种不同的预测电视覆盖的方法的原理介绍,进而时比各种方法的优劣程度,并且对每种方法可能存在的问题加以分析,提出在规划台站时选用最适合的覆盖分析方法.     

系统非线性组合预测方法及在黄河凌汛预测中应用

为了提高预测的精度,尤其是冰凌中长期预测的精度,基于工程模糊集、人工神经网络、遗传算法与组合预测理论,提出了系统非线性组合预测方法,给出了黄河内蒙段冰凌三种单一预测模型的非线性组合预测值．结果表明,所建立的非线性组合预测方法物理意义明确,数学推导过程严谨,预测精度高于任意单一预测模型．     

Correcting the January optimism effect

Each month, various professional forecasters give forecasts for next year's real gross domestic product (GDP) growth and unemployment. January is a special month, when the forecast horizon moves to the following calendar year. Instead of deleting the January data when analyzing forecast updates, I propose a periodic version of a test regression for weak-form efficiency. An application of this periodic model for many forecasts across a range of countries shows that in January GDP forecast updates are positive, whereas the forecast updates for unemployment are negative. I document that this January optimism about the new calendar year is detrimental to forecast accuracy. To empirically analyze Okun's law, I also propose a periodic test regression, and its application provides more support for this law.     

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low‐dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in‐sample predictive ability. However, the out‐of‐sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade‐off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.