开放经济条件下我国货币需求研究

文章引入股票流通市值、汇率等变量对开放经济条件下我国货币需求函数进行全面研究。协整和误差修正模型结果表明：汇率是除社会消费品零售总额外影响我国长短期货币需求稳定的最重要的变量,并且对我国货币需求的影响很大;股票市场发展对我国长期和短期货币需求的影响很小。这一发现可为我国当前反通货膨胀的货币政策的制订提供重要的参考。为控制货币供应量,我们应该重点关注汇率波动。     

发动机凸轮升程误差曲线符合“最小条件”的评定准则

无论以何种方法和何种方式所获得的升级误差曲线，均含有位置误差引起的升程误。这项误差，属系统误差，必须从检测结果予以剔除，才能真实反映凸轮的形状误差。凸轮升程误差符合最小条件的评定准则，正是为剔除置误差引起的升程误差而提出的。     

大学生英语写作中文本错误分析

文本错误指的是语言产出时犯的语法错误和词汇错误。采用定性分析的方法通过对 35 5名大学二年级学生书面作文文本错误分析 ,归纳出大学生写作中常见的 15种错误及其分布比例 ;并分析了语际错误、语内错误和其它错误三大类型的英语写作文本错误     

Development and validation of a firm-level vertical and horizontal internationalization metric

The lack of valid and reliable measures of firm-level vertical and horizontal internationalization is impeding the development and testing of hypothesized relationships between these respective dimensions of internationalization and a range of important MNE characteristics, actions, and effects. Through a series of qualitative and quantitative studies using data collected from senior MNE executives (total N?=?3,146), we develop and validate a scale to measure both vertical and horizontal firm-level internationalization. Subscales for each type of internationalization prove to be unidimensional, reliable, temporally stable, and to have predictive, cross-cultural, cross-sectoral, and discriminant validity.     

采用一维物理光学法的天线罩瞄准误差补偿

针对雷达天线罩瞄准误差的补偿问题,提出了一种适用于各向同性的天线罩瞄准误差修正方法。构建了基于一维物理光学法的天线罩瞄准误差的数学模型,推导了雷达导引头测量目标角度和角速度的误差修正公式。试验结果表明,该方法有效降低了天线罩瞄准误差对目标角速度性能的影响。     

经济开放与货币需求:国际金融风险及持币成本的测度

在中国开放经济体制下的基准货币需求模型中,本文将源于国际金融市场的持币成本设为遗漏潜变量,并构建特定的国际金融综合指数(CIFI)作为该潜变量的测度。借鉴机器学习与测度理论,本文利用对数误差修正模型提出了分步降维的CIFI构造算法,构造了长期CIFI和短期CIFI。结果表明,CIFI构造中的无监督降维步骤有助于减少高维金融数据中的冗余信息。实证分析发现,国际机会成本对中国货币需求具有规律性的前导影响,而在2007至2008年国际金融危机期间,央行的应急措施对长期CIFI所代表的非均衡冲击起到明显的阻截效果,对短期CIFI的影响基本是持续不变的。通过综合指数构造与宏观货币需求模型的算法连接,可以利用CIFI的构成结构从前导时间与影响强度两方面追踪冲击货币需求的国际金融风险的具体来源,这为宏观决策者监测国际金融市场提供了颇有规律的信息。在方法论上,本研究为如何利用模型监测国际金融市场影响宏观经济开辟了一条新路。     

Forecast-error-based estimation of forecast uncertainty when the horizon is increased

Past forecast errors are employed frequently in the estimation of the unconditional forecast uncertainty, and several institutions have increased their forecast horizons in recent times. This work addresses the question of how forecast-error-based estimation can be performed if there are very few errors available for the new forecast horizons. It extends the results of Knüppel (2014) in order to relax the condition on the data structure that is required for the SUR estimator to be independent of unknown quantities. It turns out that the SUR estimator of the forecast uncertainty, which estimates the forecast uncertainty for all horizons jointly, tends to deliver large efficiency gains relative to the OLS estimator (i.e., the sample mean of the squared forecast errors for each individual horizon) in the case of increased forecast horizons. The SUR estimator is applied to the forecast errors of the Bank of England, the US Survey of Professional Forecasters, and the FOMC.     

真实型领导与团队创新绩效: 差错管理氛围与特质调节焦点的作用研究

团队创新是企业获取竞争优势,实现可持续发展的重要保证。差错管理氛围和特质焦点提供了理解团队创新的新视角。研究发现真实型领导通过营造差错管理氛围,对团队创新绩效产生正向影响;团队提升型特质调节焦点在差错管理氛围和团队创新绩效间起到正向调节作用。文章拓展了创新、差错管理氛围以及调节焦点等相关文献,并为提升团队创新绩效提供了管理建议。     

Semiparametric time series regression modeling with a diverging number of parameters

Variable selection and error structure determination of a partially linear model with time series errors are important issues. In this paper, we investigate the regression coefficient and autoregressive order shrinkage and selection via the smoothly clipped absolute deviation penalty for a partially linear model with a divergent number of covariates and finite order autoregressive time series errors. Both consistency and asymptotic normality of the proposed penalized estimators are derived. The oracle property of the resultant estimators is proved. Simulation studies are carried out to assess the finite‐sample performance of the proposed procedure. A real data analysis is made to illustrate the usefulness of the proposed procedure as well.     

The asymptotic expansion of the regular discretization error of Itô integrals

We study an Edgeworth‐type refinement of the central limit theorem for the discretization error of Itô integrals. Toward this end, we introduce a new approach, based on the anticipating Itô formula. This alternative technique allows us to compute explicitly the terms of the corresponding expansion formula. Two applications to finance are given; the asymptotics of discrete hedging error under the Black–Scholes model and the difference between continuously and discretely monitored variance swap payoffs under stochastic volatility models.