Macroeconomic risk and the cross-section of stock returns

We develop a conditional version of the consumption capital asset pricing model (CCAPM) using the conditioning variable from the cointegrating relation among macroeconomic variables (dividend yield, term spread, default spread, and short-term interest rate). Our conditioning variable has a strong power to predict market excess returns in the presence of competing predictive variables. In addition, our conditional CCAPM performs approximately as well as Fama and French’s (1993) three-factor model in explaining the cross-section of the Fama and French 25 size and book-to-market sorted portfolios. Our specification shows that value stocks are riskier than growth stocks in bad times, supporting the risk-based story.     

A Study on Strategic Value Chain and Cost Driver and PEST Analysis Method

Strategic value chain analysis method can be adopted to analyze the existing cost causality so as to confirm and optimize the value chain. Strategic cost driver analysis method that is used on the income of the owners and daily operators, production and operating activities, administration, resources, culture, etc in an enterprise can further enlarge the strategic cost scope. For analysis of the enterprise and its external environment factor, PEST analysis is represented as the extroversive analysis of cost causality. Three analytical methods of different kinds having some commonness on cost causality analysis can be combined together to form the causality analysis method, which will expand the visual angle of analysis and help to establish the integral frame of strategic cost causality analysis.     

A Classification of Dutch Manufacturing based on a Model of Innovation

Summary The paper studies the degree of homogeneity of innovative behavior in order to determine empirically an industry classification of Dutch manufacturing that can be used for policy purposes. Defining homogeneity in terms of an economic model distinguishes our classification from existing taxonomies such as those of the OECD, Pavitt and the various classifications based on a principal components analysis. We use a two-limit tobit model with sample selection, which explains the decisions by business enterprises to innovate and the impact these decisions have on the share of innovative sales. The model is estimated for eleven industries based on the Dutch Standard Industrial Classification (SBI 1993). A likelihood ratio (LR) test is then performed to test for equality of the parameters across industries. We find that Dutch manufacturing consists of three groups of industries in terms of innovative behavior, a high-tech group, a low-tech group and the industry of wood. The same pattern shows up in the three Dutch Community Innovation Surveys. The empirical part of this study has been carried out at the Centre for Research of Economic Microdata at Statistics Netherlands. The authors wish to thank Statistics Netherlands, and in particular Bert Diederen, for helping us in accessing and using the Micronoom data set. The views expressed in this paper are solely those of the authors. The authors also wish to thank Fran?ois Laisney, Patrick Waelbroek and participants at presentations in Maastricht, Strasbourg, Leuven and Lille for their helpful comments. The first author acknowledges financial support from METEOR.     

混合地理加权回归模型的拟合及相关性检验

利用两步估计方法对混合地理加权回归模型进行拟合,运用Moran's检验方法探索误差项的空间相关性。     

基于深度学习模型的种植结构复杂区农作物精细分类研究

目的 卫星遥感技术具有覆盖范围广、探测周期短、调查成本低等优势而广泛应用于大区域农作物分类。然而在种植结构复杂区（如城乡结合部）,因其地块破碎、同期生长的作物种类多且分布分散,利用传统的统计分类或机器学习方法进行农作物分类时仍存在精度不高的问题。为提高种植结构复杂区农作物分类精度。方法 文章选取河北省廊坊市广阳区为研究区,以GF-1 PMS全色多光谱融合影像为数据源,采用U-Net、PSPNet及DeepLabv3+,3种深度学习模型进行农作物分类研究。分析模型参数对农作物分类精度的影响,评价3种深度学习模型的农作物分类精度,优选农作物精细分类方法。结果 （1）学习率与3种深度学习模型的分类精度呈正相关关系,较大的学习率（0.01,0.001）下,3种模型收敛速度快,分类精度高。批样本量与模型分类稳定性相关,批样本量设为100时,3种模型的分类稳定性最好。（2）相比PSPNet、DeepLabv3+模型,U-Net模型分类效果最好,总体分类精度为89.32%。（3）GF-1 PMS影像结合U-Net模型可有效提升种植结构复杂区农作物分类精度,大宗作物春玉米、夏玉米的分类精度在80%以上,花生、红薯、蔬菜小宗作物分类精度在60%以上。结论 该研究可为准确获取种植结构复杂区的农作物类型、面积及空间分布信息提供参考依据。     

环境质量变化对游客福利的影响——基于旅行费用和条件行为联合数据

环境质量是影响游客旅游需求及福利的重要因素,但单一使用陈述性偏好法或揭示性偏好法仍存在诸多局限性。为探究环境质量改变对游客需求及福利变化的具体影响,结合旅行费用与条件行为数据,在明确现有环境质量状况下景区价值基础上,评估环境质量改善与恶化两种情景下,游客消费者剩余的变化。结果表明,环境质量改变直接影响旅行次数,从而增加或减少消费者剩余,但不会通过影响游客旅行费用间接改变消费者剩余,实际行为与条件行为数据偏好存在一致性;白洋淀景区当前消费者剩余为909.09元/次;环境质量改善使游客平均旅行次数增加1.45次/年,并使每位游客每年的消费者剩余增加1 318.18元;环境质量恶化使游客平均旅行次数降低1.41次/年,并使每位游客每年的消费者剩余减少1 281.81元。     

工程物探技术在地下库可研阶段勘察中的应用

根据某大型地下水封石油油库可研阶段勘察实施过程,介绍大型地下水封石油油库可研阶段工程勘察的要求和方法、工程物探种类、高密度电法和地震反射法的原理、方法、实施过程中的应用情况、勘察成果整理分析。阐述了物探与钻探、地质测绘相互验证对建库条件的评价。     

利用无人机标定雷达精度的新方法

针对现有雷达标定系统存在的使用成本高、组织实施不方便、真值获取手段不可靠等一系列问题,提出了利用搭载“北斗”卫星定位系统的旋翼无人机作为探测目标的三坐标雷达精度标定新方法。设计了基于该方法的三坐标雷达标定系统方案,研究了系统实现所需的关键技术,最后给出了系统样机的试验结果。结果表明,该方法能够真实反映三坐标雷达的系统误差,与传统雷达标定方法相比,更加经济、简便、灵活。     

我国15个副省级城市产业结构分析——基于大连的视角

在产业结构理论基础上,建立了偏离份额分析方法的数学模型,通过15个副省级城市的产业数据处理,比较分析了大连的产业结构情况。结果显示,大连经济增长主要依赖全国效应,经济中第二产业的优势最为突出,第三产业发展成效不大。     

采用积分二次相位函数的雷达脉内调制类型识别

为了在低信噪比条件下获得较好的识别准确率,提出了一种基于积分二次相位函数（IQPF）的识别分类方法。根据不同信号的调频斜率的正负值完成信号的预分类,然后再利用不同信号的IQPF峰值-能量比以及功率谱估计实现信号的自动化分类。给出了识别流程图并进行了仿真实验,结果表明,该方法在信噪比大于-3dB时,其识别正确率达到90%以上,证明了方法的有效性和可行性。相关研究为雷达脉内调制类型的识别提供了一种切实可行的方法,具有一定的应用价值。