Cointegration as an explanation for the Meese–Rogoff puzzle

Some economists suggest that the failure of exchange-rate models to outperform the random walk in exchange rate forecasting out of sample can be attributed to failure to take into account cointegration when it is present. We attempt to find out if cointegration matters for forecasting accuracy by examining the relation between the stationarity and size of the forecasting error. Results based on three macroeconomic models of exchange rates do not provide strong support for the proposition that cointegration matters for forecasting accuracy. The simulation results show that while stationary errors tend to be smaller than non-stationary errors, this is not a universal rule. Irrespective of the presence or absence of cointegration, none of the three models can outperform the random walk in out-of-sample forecasting, which means that cointegration cannot solve the Meese–Rogoff puzzle.     

Forecasting US consumer price index: does nonlinearity matter?

The objective of this article is to predict, both in sample and out of sample, the consumer price index (CPI) of the US economy based on monthly data covering the period of 1980:1–2013:12, using a variety of linear (random walk (RW), autoregressive (AR) and seasonal autoregressive integrated moving average (SARIMA)) and nonlinear (artificial neural network (ANN) and genetic programming (GP)) univariate models. Our results show that, while the SARIMA model is superior relative to other linear and nonlinear models, as it tends to produce smaller forecast errors; statistically, these forecasting gains are not significant relative to higher-order AR and nonlinear models, though simple benchmarks like the RW and AR(1) models are statistically outperformed. Overall, we show that in terms of forecasting the US CPI, accounting for nonlinearity does not necessarily provide us with any statistical gains.     

Examining technological competition between BMW and Hyundai in the Korean car market

The Korean car market has increased in size. BMW and Hyundai are the top-ranked imported and domestic vehicle brands in Korea, respectively. Thus, it is important to understand these companies and the Korean car market, because technology is most significant in the vehicle industry. In this paper, we compare BMW with Hyundai from the technological perspective. Our research is focused on an analysis of the technological competition between BMW and Hyundai based on their developed technologies. We use all BMW and Hyundai patents from worldwide patent databases to analyse the two companies’ technologies. In addition, we apply statistical methods and machine learning algorithms to the patent analysis. In our conclusion, we show the technological differences and competition between BMW and Hyundai, and find their relative strengths and weaknesses.     

高层剪力墙结构问题的探析

高层剪力墙结构形式和布局要适度合理,需要按照“强剪弱弯”、“强柱弱梁”的总体建筑理念设计。可采用三维杆系的薄壁柱空间分析方法或墙组元的分析方法进行计算,但对墙肢长的剪力墙,应该用空间杆的墙组元程序进行校验。异形柱结构是多肢的,其延性较差,由于截面的特殊性,其各项承载能力也有较大差异。高层剪力墙结构通过恰当的布局、设计,达到高层建筑荷载力量均衡的目标。     

论Lab-Corder测量太阳电池伏安特性的实验技巧

在Lab-Corder测量太阳电池伏安特性实验中,只要改变二个可变电位器的负载大小,就可以完成本实验的数据测量.但Lab-Corder采集到的数据及图形往往不是很理想,达不到预期的结果.在实验过程中如何进行才能得到我们想要的实验结果呢？在本实验中实验的技巧是至关重要的一环,本文重点探讨其实验的技巧及方式方法对实验的结果影响.     

曹妃甸软土地基偏桩现象分析与处理措施

曹妃甸图书行政楼预制管桩基础发生偏桩事故,经分析偏桩产生的主要原因是基坑周围动荷载过大,引起桩身整体偏移。对此采用针对性处理措施,使之纠偏。实践证明,处理措施是正确的。     

断路器检修方式及特性仪器的改进

文章介绍了目前供电企业高压断路器的检修方式,以及断路器特性数据的采集方式。这种方式需要安排停电,不仅影响供电可靠性还造成企业经济损失。最为关键的是在停电情况下进行的测试,并不能客观的反映断路器工作时的运行状态。文章提出了一种新的数据采集方式,利用断路器在分合闸时产生的电磁干扰来采集其工作状态时的特性数据,从而得到更客观有价值的数据,再决定该断路器的检修方案。     

基于灰色神经网络的咸宁市财政收入预测研究

在介绍灰色神经网络模型原理的基础上,以湖北省咸宁市为例,利用灰色神经网络模型预测出2011-2014年咸宁市财政收入,由于灰色神经网络模型考虑了多指标的共同作用,具有更好的预测效果。     

基于小波变换和支持向量的国际原油价格预测

本文将小波分析与支持向量回归结合应用于国际原油价格预测,通过小波多尺度分析方法将油价时间序列分解为长期趋势和随机扰动项,然后采用支持向量回归对分解后的油价长期趋势进行预测。油价长期趋势的预测采用多因素预测方法,主要考虑市场供需基本面、库存、经济、投机等因素对石油价格走势的影响,建立多输入单输出的支持向量回归模型。实证研究表明,支持向量回归模型具有较高的预测性能,对原油价格长期趋势预测中,该方法比回归方法的预测精度高。     

不同负荷对女子链球旋转技术的运动学研究

本文通过对陕西省王峥、张莉、张叶伟等省队优秀女子运动员采用文献法、专家访谈法、生物力学测试方法和数理统计法等研究方法对投掷不同重量铅球的动作技术进行分析,结果表明:①不同负荷对链球旋转投掷技术在各个阶段的运动学参数存在差异。②不同负荷在教学和训练过程中应该采用相对的质量。③不同负荷在链球投掷训练过程中,建议选球重量与标准球重量差异不超过1.5公斤,否则可能对标准球投掷技术的技术结构与技术节奏产生负面影响。