Intersectoral wage linkages: the case of Sweden

The purpose of this study is to investigate whether wage-setting in certain sectors of the Swedish economy affects wage-setting in other sectors. The theoretical background is the Scandinavian model of inflation, which states that wage-setting in the sectors exposed to international competition lead wage-setting in the sheltered sectors of the economy. The Johansen maximum likelihood cointegration approach is applied to quarterly data on Swedish sector wages for the period 1980:1–2002:2. Different vector error correction (VEC) models are created, based on assumptions as to which sectors are exposed to international competition and which are not. Granger causality tests are then carried out in the different restricted/unrestricted VEC models to test for sector wage leadership. The Granger causality tests provide strong evidence for the presence of intersectoral wage causality, but no evidence of a wage-leading role for the internationally exposed manufacturing sector.      

重心位置误差对隔振系统性能的影响

论述了机载电子设备的重心位置与隔振系统的几何中心不重合误差对其安装架隔振系统性能的影响，并提出了解决该问题的方法。     

中部地区固定资产投资与经济增长关系的实证分析——以阜阳市为例

本文对阜阳市固定资产投资与经济增长之间的关系进行了定量分析,通过建立误差修正模型来反映二者之间长期均衡和短期波动关系,分析固定资产投资对当地经济增长的贡献水平,结果表明固定资产投资能有效的拉动当地经济增长。     

Are Corporate Restructuring Events Driven by Common Factors? Implications for Takeover Prediction

Abstract:  The paper shows that variables commonly used in takeover prediction models also help to explain the likelihood of several other restructuring events, including divestitures, bankruptcies and significant employee layoffs. This finding helps to explain the larger misclassification errors in binomial takeover prediction models commonly used in prior research. The results show that modelling takeover prediction models in a binomial setting is likely to lead to misspecification in the parameter estimates and, further, result in erroneous conclusions about the determinants of takeover likelihood. The paper shows that controlling for other restructuring events by using a multinomial framework results in consistently lower misclassification errors in out-of-sample prediction tests, when compared to the benchmark of a typical binomial model.     

供应商供应绩效评价指标体系的建立与控制

随着供应链管理理论的发展，企业角色及外部需求的变化，竞争的加剧，以及信息技术飞速发展所带来的革命性影响。供应商供应绩效评价受到了越来越广泛的关注和日益深入的讨论并逐步在实践中采用。文章主要对供应商绩效评价指标体系进行研究，目的是能够符合对供应商的动态跟踪评价，同时有助于对供应商的有效控制。     

FDI与湖南产业结构关系的实证研究

对湖南的产业结构和三次产业利用FDI的现状进行了分析,利用1983～2006年的统计数据,运用协整分析技术、误差修正模型和Cranger因果关系检验,对FDI与产业结构变动之间的关系进行了实证研究.结果表明,FDI与产业结构变动之间存在正向的协整关系,FDI是产业结构变动的重要原因;在短期FDI偏离了产业结构的长期均衡水平,但短期波动对长期均衡的回调速度较快.进而,就模型得出的结论提出了若干建议.     

一种新的基于交互多模型的序贯重要采样算法

通过将交互多模型（IMM）算法和粒子滤波（SIS）算法结合，提出了一种新的IMM～SIS算法。在每个模型中，都有一个标准的粒子滤波器，模型之间的交互与传统的IMM一样。由于在新的算法中，每个模型中粒子滤波都保证固定数量的粒子，因此不会出现粒子退化和贫乏现象。仿真证明了新的IMM—SIS算法在收敛速度和精度方面都要优于传统的IMM—EKF算法。     

农业FDI与农产品出口结构优化——基于协整和误差修正模型的实证分析

文章利用我国1984—2006年农业FDI的样本数据,就农业FDI与农产品出口结构之间的关系作了实证分析。协整检验表明二者之间存在长期的均衡关系,农业FDI是农产品出口结构优化的格兰杰原因;误差修正模型显示短期内农业FDI对农产品出口结构通常存在滞后1年的影响。对二者之间的关系进行分析,有利于政府部门和农业企业在利用FDI优化农产品出口结构过程中采取合理对策。     

The unintended consequences of grouping in tests of asset pricing models

We identify a number of unintended consequences of grouping when the capital asset pricing model is true and when it is false. When the model is true, grouping may cause fundamental problems with the most basic capital asset pricing and cross-sectional regression relationships. For example, with traditional grouping, the market portfolio is super-efficient––unless securities in each group are value weighted. Yet, when the model is grossly false, grouping may cause the model to appear to be absolutely correct. Ironically, the only way this can occur is when securities in each group are value weighted. To make matters worse, when the model is false, the slope of a cross-sectional regression of expected returns on betas fitted to grouped data may be either steeper or flatter than when the regression is fitted to ungrouped data. In other words, grouping may exacerbate the very problem it was meant to alleviate.     

Diagnostics cannot have much power against general alternatives

Model diagnostics are shown to have little power unless alternative hypotheses can be narrowly defined. For example, the independence of observations cannot be tested against general forms of dependence. Thus, the basic assumptions in regression models cannot be inferred from the data. Equally, the proportionality assumption in proportional-hazards models is not testable. Specification error is a primary source of uncertainty in forecasting, and this uncertainty will be difficult to resolve without external calibration. Model-based causal inference is even more problematic.