外语教师队伍建设要重视造就学者型教师

外语教师队伍建设要从改变教师的传统教育理念,加强专业理论知识学习,提高教师的反思意识,研究教师的发展模式等方面入手,引导培养青年教师向学者型教师发展。     

中国股票市场的信息反应曲线和股票价格波动的非对称性

股票价格波动对于市场信息的反应过程具有非对称性。通过利用多种非对称性GARCH 模型,描述和检验了沪市股票日收益率序列的条件波动性,并通过对股票市场信息影响曲线的分析,发现沪市股票价格波动中存在显著的非对称性反应。这说明股市波动对于不同的政策干预和信息冲击具有不同程度的反应,“利好消息”对股市的刺激作用仍然需要其他市场干预的配合才能发挥出来。     

股票市场的极值风险测度及后验分析研究

通过对上证综指和世界股市若干重要指数的实证研究发现,无论是在成熟资本市场还是新兴资本市场当中,极值理论(EVT)及其工具都能更加准确地刻画实际市场的极端波动和风险状况.详细说明了不同收益分布假定下风险价值(VaR)的计算方法及其后验分析(Back-testing)过程,证明了与非条件和条件正态分布以及条件t分布等主流金融理论的收益分布假定相比,条件EVT分布在测度极端市场风险时所表现出的优越性,同时说明了在不同概率水平下各种收益分布假定的精确度和适用范围.     

部分中间产出作为最终产品的两阶段合作效率

网络DEA模型研究多个进程和多个子系统的复杂系统的效率度量.针对中间产出无法为第2阶段完全消耗的情形,传统DEA模型对系统效率的评价偏高,已有网络DEA模型则对系统效率的评价偏低.本文提出部分中间产出作为最终产品,进入流通渠道的两阶段效率模型,用来测度两阶段生产过程的真实效率.采用乘积形式描述两阶段合作的特征,给出不同...     

区域电力市场结构、所有权结构对发电企业效率影响的实证研究

以我国电力产业放松规制的实践为背景,基于产业组织理论构建了区域电力市场结构、所有权结构对发电企业效率影响的综合模型。以1999~2006年电力行业上市公司的统计数据为样本,采用分层线性模型回归方法对市场结构和所有权结构对发电企业效率的影响效果和影响路径进行了研究。实证结果表明:电力行业市场集中度下降有利于我国电力企业效率提升;电力行业国有所有权比重下降有利于企业效率的提升;电力行业企业数量和国有所有权比重对企业效率存在负向交互影响作用,但不显著。     

Program Evaluation and Causal Inference With High‐Dimensional Data

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data‐rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function‐valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced‐form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post‐regularization and post‐selection inference that are uniformly valid (honest) across a wide range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced‐form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment‐condition framework, which arises from structural equation models in econometrics. Here, too, the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function‐valued) parameters within this general framework where any high‐quality, machine learning methods (e.g., boosted trees, deep neural networks, random forest, and their aggregated and hybrid versions) can be used to learn the nonparametric/high‐dimensional components of the model. These include a number of supporting auxiliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity‐based estimation of regression functions for function‐valued outcomes.     

Value drivers of social businesses: A business model perspective

Hybrid organizational forms that combine commercial and welfare institutional logics play an increasingly important role in addressing the grand societal challenges we face today. Building on the literatures on hybrid organizations and social business models, we explore the characteristics of social businesses from a business model perspective. This study seeks to better understand the particularities and value drivers of hybrid social purpose in contrast to purely commercial business models. We follow a grounded theory approach and our findings are based on interview data from 17 social business firms. Building on social businesses' identified particularities, we propose four value drivers of social business models: 1) responsible efficiency, 2) impact complementarities, 3) shared values, and 4) integration novelties. We link our findings to the literature, contributing new insights into social businesses models and implications for practitioners.     

Let's Call it Quits: Break‐Even Effects in the Decision to Stop Taking Risks

“Chasing” behavior, whereby individuals, driven by a desire to break even, continue a risky activity (RA) despite incurring large losses, is a commonly observed phenomenon. We examine whether the desire to break even plays a wider role in decisions to stop engaging in financially motivated RA in a naturalistic setting. We test hypotheses, motivated by this research question, using a large data set: 707,152 transactions of 5,379 individual financial market spread traders between September 2004 and April 2013. The results indicate strong effects of changes in wealth around the break‐even point on the decision to cease an RA. An important mediating factor was the individual's historical long‐term performance. Those with a more profitable trading history were less affected by a fall in cash balance below the break‐even point compared to those who had been less profitable. We observe that break‐even points play an important role in the decision of nonpathological risk takers to stop RAs. It is possible, therefore, that these nonpathological cognitive processes, when occurring in extrema, may result in pathological gambling behavior such as “chasing.” Our data set focuses on RAs in financial markets and, consequently, we discuss the implications for institutions and regulators in the effective management of risk taking in markets. We also suggest that there may be a need to consider carefully the nature and role of “break‐even points” associated with a broader range of nonfinancially‐focused risk‐taking activities, such as smoking and substance abuse.     

A Framework to Understand Extreme Space Weather Event Probability

An extreme space weather event has the potential to disrupt or damage infrastructure systems and technologies that many societies rely on for economic and social well‐being. Space weather events occur regularly, but extreme events are less frequent, with a small number of historical examples over the last 160 years. During the past decade, published works have (1) examined the physical characteristics of the extreme historical events and (2) discussed the probability or return rate of select extreme geomagnetic disturbances, including the 1859 Carrington event. Here we present initial findings on a unified framework approach to visualize space weather event probability, using a Bayesian model average, in the context of historical extreme events. We present disturbance storm time (Dst ) probability (a proxy for geomagnetic disturbance intensity) across multiple return periods and discuss parameters of interest to policymakers and planners in the context of past extreme space weather events. We discuss the current state of these analyses, their utility to policymakers and planners, the current limitations when compared to other hazards, and several gaps that need to be filled to enhance space weather risk assessments.     

Tractable Consideration Set Structures for Assortment Optimization and Network Revenue Management

Discrete‐choice models are widely used to model consumer purchase behavior in assortment optimization and revenue management. In many applications, each customer segment is associated with a consideration set that represents the set of products that customers in this segment consider for purchase. The firm has to make a decision on what assortment to offer at each point in time without the ability to identify the customer's segment. A linear program called the Choice‐based Deterministic Linear Program (CDLP) has been proposed to determine these offer sets. Unfortunately, its size grows exponentially in the number of products and it is NP‐hard to solve when the consideration sets of the segments overlap. The Segment‐based Deterministic Concave Program with some additional consistency equalities (SDCP+) is an approximation of CDLP that provides an upper bound on CDLP's optimal objective value. SDCP+ can be solved in a fraction of the time required to solve CDLP and often achieves the same optimal objective value. This raises the question under what conditions can one guarantee equivalence of CDLP and SDCP+. In this study, we obtain a structural result to this end, namely that if the segment consideration sets overlap with a certain tree structure or if they are fully nested, CDLP can be equivalently replaced with SDCP+. We give a number of examples from the literature where this tree structure arises naturally in modeling customer behavior.