Functional and emotional traits of corporate social media message strategies: Behavioral insights from S&P 500 Facebook data

Drawing from literature regarding public engagement, the Elaboration Likelihood Model (ELM), computer-mediated communication research, and emotion psychology, this study examines the effects of companies’ social media communication strategies on public engagement behaviors as indexed by post likes, shares, and comments. Specifically, it investigates how corporate Facebook posts’ functional traits (functional interactivity and vividness) and emotional traits (emotion presence, valence, and strength) impact public engagement online. Through data mining and computer-assisted sentiment analysis of 33,379 posts from 106 Standard & Poor 500 companies’ Facebook accounts, this study finds a negative effect of functional interactivity but a positive effect of vividness on engagement. It also shows that emotional traits overall yield stronger public engagement outcomes. Two-way interactions between emotional and functional features are also detected. Theoretical and practical implications are discussed.     

基于产品生命周期的多阶段剩余收益项目决策模型

剩余收益模型（Residual Income Model，RIM）是评估公司权益价值的经典模型，特别地，奥尔森系列剩余收益模型由于可以利用历史财务与会计数据而得到广泛运用。本文在一般剩余收益模型的基础上，运用规范研究、逻辑推理等方法，基于产品生命周期理论，即项目处于产品生命周期的不同阶段时，其净资产收益率不同，在增长期的净资产收益率持续上升，衰退期的净资产收益率持续下降，而成熟期的净资产收益率则围绕行业平均水平波动，从理论上就项目投资价值的评估构建了多阶段剩余收益项目决策模型，并就所构建的多阶段剩余收益项目决策模型进行参数赋值分析。研究结果表明该新构建的决策模型具有较好的理论和运用价值，可以作为一种有效的项目投资决策模型。本文的研究一是拓展了剩余收益模型的应用，从主要用于对公司权益价值的估值拓展至对项目投资决策的评估；二是有利于寻求项目投资决策的价值最大化。     

Testing for Serial Independence: Beyond the Portmanteau Approach

Portmanteau tests are typically used to test serial independence even if, by construction, they are generally powerful only in presence of pairwise dependence between lagged variables. In this article, we present a simple statistic defining a new serial independence test, which is able to detect more general forms of dependence. In particular, differently from the Portmanteau tests, the resulting test is powerful also under a dependent process characterized by pairwise independence. A diagram, based on p-values from the proposed test, is introduced to investigate serial dependence. Finally, the effectiveness of the proposal is evaluated in a simulation study and with an application on financial data. Both show that the new test, used in synergy with the existing ones, helps in the identification of the true data-generating process. Supplementary materials for this article are available online.     

金融周期、金融波动与中国经济增长——基于省际面板门槛模型的研究

本文应用面板门槛模型，研究了2005-2017年间我国31个省（市、自治区）金融波动对经济增长的影响。研究发现，随着金融周期所处阶段的变化，金融波动对经济增长会产生显著的非对称性双重门槛效应，主要体现为如下两点：首先，金融周期处于膨胀期、平稳期和萧条期时金融波动对经济增长会产生负向影响，但从影响系数值的大小来看，处于膨胀期时最大，是后两者的2倍之多，处于平稳期时最小且并不显著；其次，分区域的稳健性检验表明，金融发展水平高的区域双重门槛值出现得早，且两个门槛值间的区间要比金融发展水平低的区域宽28%。这些结论说明，经济增长对于金融波动的容忍弹性会随着金融周期所处的阶段而变化，金融发展水平的提高会放大经济增长对金融波动的容忍区间，但也会加速金融周期处于膨胀期时爆发金融危机的可能性，这使得当前我国存在着进一步发展金融水平和严控金融风险的矛盾，对此本文也提出了相应的政策建议。     

Control Chart for Monitoring Autocorrelated Process with Multiple Exogenous Inputs

We propose to use AR-Sieve Bootstrap in the construction of a control chart of an autocorrelated process influenced by multiple exogenous inputs. The control charts are compared with Exponentially Weighted Moving Average (EWMA) control chart through a simulation study. AR-Sieve bootstrap control limits are narrower than EWMA control limits. While the proposed method yields a higher rate of false alarms, it is quick in detecting even minimal structural changes.     

空气污染治理与公众健康———来自“大气十条”政策的证据

为探究《大气污染防治行动计划》 (以下简称“大气十条”)的健康效应,本文基于中国健康与营养调查(CHNS)数据及省级面板数据,利用基于多项Logit模型的双重差分法(Logit-DID),从微观与宏观两个维度考察“大气十条”政策的健康改善效应及其传导机制,并进一步通过异质性分析探讨环境健康不平等的内在原因 研究发现:“大气十条”实施带来显著的健康效应,该政策不仅能有效控制与空气污染高度相关疾病的发病率和死亡占比,还能够间接降低受空气污染影响较小的疾病病情。其作用机理主要通过控制PM2.5浓度改善公众健康,同时该政策对二氧化硫(SO2)、氮氧化物、烟(粉)尘等其 他污染物浓度的下降起到了协同作用。进一步分析显示环境福利不公平的现象依然存在,“大气十条”政 策显著降低了疾病高发人群的发病率,且对女性、城镇居民和低、高年龄段等疾病高发人群的健康影响更显著。本文为政府构建环境健康公平发展路径,推进“健康中国”战略提供了经验证据和政策借鉴。     

Sparse group lasso for multiclass functional logistic regression models

Sparsity-inducing penalties are useful tools for variable selection and are also effective for regression problems where the data are functions. We consider the problem of selecting not only variables but also decision boundaries in multiclass logistic regression models for functional data, using sparse regularization. The parameters of the functional logistic regression model are estimated in the framework of the penalized likelihood method with the sparse group lasso-type penalty, and then tuning parameters for the model are selected using the model selection criterion. The effectiveness of the proposed method is investigated through simulation studies and the analysis of a gene expression data set.     

The embeddedness of organizational performance: Multiple Membership Multiple Classification Models for the analysis of multilevel networks

We present a Multiple Membership Multiple Classification (MMMC) model for analysing variation in the performance of organizational sub-units embedded in a multilevel network. The model postulates that the performance of organizational sub-units varies across network levels defined in terms of: (i) direct relations between organizational sub-units; (ii) relations between organizations containing the sub-units, and (iii) cross-level relations between sub-units and organizations. We demonstrate the empirical merits of the model in an analysis of inter-hospital patient mobility within a regional community of health care organizations. In the empirical case study we develop, organizational sub-units are departments of emergency medicine (EDs) located within hospitals (organizations). Networks within and across levels are delineated in terms of patient transfer relations between EDs (lower-level, emergency transfers), hospitals (higher-level, elective transfers), and between EDs and hospitals (cross-level, non-emergency transfers). Our main analytical objective is to examine the association of these interdependent and partially nested levels of action with variation in waiting time among EDs – one of the most commonly adopted and accepted measures of ED performance. We find evidence that variation in ED waiting time is associated with various components of the multilevel network in which the EDs are embedded. Before allowing for various characteristics of EDs and the hospitals in which they are located, we find, for the null models, that most of the network variation is at the hospital level. After adding these characteristics to the model, we find that hospital capacity and ED uncertainty are significantly associated with ED waiting time. We also find that the overall variation in ED waiting time is reduced to less than a half of its estimated value from the null models, and that a greater share of the residual network variation for these models is at the ED level and cross level, rather than the hospital level. This suggests that the covariates explain some of the network variation, and shift the relative share of residual variation away from hospital networks. We discuss further extensions to the model for more general analyses of multilevel network dependencies in variables of interest for the lower level nodes of these social structures.     

气候变化政治对海湾国家的影响

海湾地区气候炎热,淡水资源缺乏。长期以来,海湾国家主要依靠能源出口获取大量资金,借此修建海水淡化工厂等各种设施以适应和改善气候环境的影响。近年来,国际社会在应对全球变暖问题的协调合作中,已形成一种处理全球气候变化的政治。气候变化政治要求各国减少能源消耗和降低二氧化碳排放量以缓解全球变暖趋势,从而对海湾国家以能源出口收益来维护生存环境和社会稳定的基本经济模式构成冲击。面对气候变化政治带来的冲击,海湾国家目前的权宜选择只能是继续扩大能源出口,并尽量拖延实施气候变化协议来进行应对。然而,这种做法虽可以暂时保持海湾国家改善环境的努力和维护其经济政治结构稳定,但却难以消除全球气候变化政治今后长期推动能源结构调整和节能减排所带来的压力。     

An m-estimation-based model selection criterion with a data-oriented penalty

In Wu and Zen (1999), a linear model selection procedure based on M-estimation is proposed, which includes many classical model selection criteria as its special cases, and it is shown that the selection procedure is strongly consistent for a variety of penalty functions. In this paper, we will investigate its small sample performances for some choices of fixed penalty functions. It can be seen that the performance varies with the choice of the penalty. Hence, a randomized penalty based on observed data is proposed, which preserves the consistency property and provides improved performance over a fixed choice of penalty functions.