企业资源配置战略与盈余持续性

盈余持续性是盈余质量的重要特征。较高的盈余持续性预示着企业的盈余质量较高,在一定程度上表明企业的经营状况平稳并具有良好的管理水平,有利于企业降低经营风险、提升公司价值。近年来,盈余持续性已经成为资本市场研究领域的一个热点问题。以我国A股市场2007—2017年非金融类上市公司为样本,对企业资源配置战略与盈余持续性之间的关系进行了实证研究。研究结果表明,经营主导型公司的盈余持续性要显著强于投资主导型公司;进一步区分具体的盈余构成后发现,经营主导型公司核心利润的持续性显著强于投资主导型公司,但其投资收益的持续性显著弱于投资主导型公司。上述结论表明,盈余及其构成的持续性均会受到公司资源配置战略的影响。相关研究结论既为公司资源配置战略影响盈余特征提供了经验证据,又对外部利益相关者透过战略本质看清会计信息的内在价值具有一定的启示作用。     

Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit

In the quest to model various phenomena, the foundational importance of parameter identifiability to sound statistical modeling may be less well appreciated than goodness of fit. Identifiability concerns the quality of objective information in data to facilitate estimation of a parameter, while nonidentifiability means there are parameters in a model about which the data provide little or no information. In purely empirical models where parsimonious good fit is the chief concern, nonidentifiability (or parameter redundancy) implies overparameterization of the model. In contrast, nonidentifiability implies underinformativeness of available data in mechanistically derived models where parameters are interpreted as having strong practical meaning. This study explores illustrative examples of structural nonidentifiability and its implications using mechanistically derived models (for repeated presence/absence analyses and dose–response of Escherichia coli O157:H7 and norovirus) drawn from quantitative microbial risk assessment. Following algebraic proof of nonidentifiability in these examples, profile likelihood analysis and Bayesian Markov Chain Monte Carlo with uniform priors are illustrated as tools to help detect model parameters that are not strongly identifiable. It is shown that identifiability should be considered during experimental design and ethics approval to ensure generated data can yield strong objective information about all mechanistic parameters of interest. When Bayesian methods are applied to a nonidentifiable model, the subjective prior effectively fabricates information about any parameters about which the data carry no objective information. Finally, structural nonidentifiability can lead to spurious models that fit data well but can yield severely flawed inferences and predictions when they are interpreted or used inappropriately.     

A Simple Toxicokinetic Model Exhibiting Complex Dynamics and Nonlinear Exposure Response

Uncertainty in model predictions of exposure response at low exposures is a problem for risk assessment. A particular interest is the internal concentration of an agent in biological systems as a function of external exposure concentrations. Physiologically based pharmacokinetic (PBPK) models permit estimation of internal exposure concentrations in target tissues but most assume that model parameters are either fixed or instantaneously dose-dependent. Taking into account response times for biological regulatory mechanisms introduces new dynamic behaviors that have implications for low-dose exposure response in chronic exposure. A simple one-compartment simulation model is described in which internal concentrations summed over time exhibit significant nonlinearity and nonmonotonicity in relation to external concentrations due to delayed up- or downregulation of a metabolic pathway. These behaviors could be the mechanistic basis for homeostasis and for some apparent hormetic effects.     

Investigating the impact of violations of the “left and top means first” heuristic on response behavior and data quality

ABSTRACT

Web surveys are an established data collection mode that use written language to provide information. The written language is accompanied by visual elements, such as presentation formats and shapes. However, research has shown that visual elements influence response behavior because respondents sometimes use interpretive heuristics to make sense of the visual elements. One such heuristic is the ‘left and top means first’ (LTMF) heuristic, which suggests that respondents tend to believe that a response scale consistently runs from left to right or from top to bottom. We conducted a web survey experiment to investigate how violations of the LTMF heuristic affect response behavior and data quality. For this purpose, a random half of respondents received response options that followed a consistent order and the other half received response options that followed an inconsistent order. The results reveal significantly different response distributions between the two groups. We also found that inconsistently ordered response options significantly increase response times and decrease data quality in terms of criterion validity. We, therefore, recommend using options that follow the design strategies of the LTMF heuristic.     

Narrative persuasion,identification, attitudes,and trustworthiness in crisis communication

The purpose of this study is to test the effects of narratives in crisis communication. This research assesses how organizations benefit from using stories in their media responses, relative to sharing nonnarrative information. The theory of crisis response narratives (Heath, 2004) holds that ethical narratives are effective because they enhance trustworthiness, attitudes toward the spokesperson, and identification with the spokesperson. Normative crisis communication theory exhorts disclosing truthful information rather than spinning. In an online experiment, participants (N = 365) watched a news interview in which a scandalized company’s spokesperson responded to a journalist’s questions with (a) ethical narratives, (b) unethical (spin) narratives, or (c) nonnarrative information. Multiple mediator modeling assessed identification with the spokesperson, attitudes toward the spokesperson, and perceived trustworthiness of the spokesperson. Results indicate ethical narratives are more effective than unethical narratives. However, nonnarrative information most effectively enhances trustworthiness and bolsters identification.     

Ebola Virus Dose Response Model for Aerosolized Exposures: Insights from Primate Data

This study develops dose–response models for Ebolavirus using previously published data sets from the open literature. Two such articles were identified in which three different species of nonhuman primates were challenged by aerosolized Ebolavirus in order to study pathology and clinical disease progression. Dose groups were combined and pooled across each study in order to facilitate modeling. The endpoint of each experiment was death. The exponential and exact beta-Poisson models were fit to the data using maximum likelihood estimation. The exact beta-Poisson was deemed the recommended model because it more closely approximated the probability of response at low doses though both models provided a good fit. Although transmission is generally considered to be dominated by person-to-person contact, aerosolization is a possible route of exposure. If possible, this route of exposure could be particularly concerning for persons in occupational roles managing contaminated liquid wastes from patients being treated for Ebola infection and the wastewater community responsible for disinfection. Therefore, this study produces a necessary mathematical relationship between exposure dose and risk of death for the inhalation route of exposure that can support quantitative microbial risk assessment aimed at informing risk mitigation strategies including personal protection policies against occupational exposures.     

Tweeting about emergency: A semantic network analysis of government organizations’ social media messaging during Hurricane Harvey

While social media like Twitter have been increasingly adopted by public-sector organizations, it remains less explored as to how government and emergency management (EM) organizations use these platforms to communicate with the public in response to emerging natural disasters. Extending the Situational Crisis Communication Theory (SCCT) to the realm of social media, this study examines the emerging semantic networks from 67 government and EM organizations’ official tweets during Hurricane Harvey over a three-week period. It identifies how multiple crisis response strategies—including instructing information, adjusting information, and bolstering—are constituted of different issues, actions, and organizational actors before, during, and immediately after the disaster event. Results suggest that government agencies use the strategy of instructing information predominantly before and during the disaster, whereas adjusting information and bolstering strategies are utilized more during post-disaster recovery. The study offers theoretical and practical implications of using a semantic network approach to studying organizational crisis responses.     

Inferences for a Partially Varying Coefficient Model With Endogenous Regressors

In this article, we propose a new class of semiparametric instrumental variable models with partially varying coefficients, in which the structural function has a partially linear form and the impact of endogenous structural variables can vary over different levels of some exogenous variables. We propose a three-step estimation procedure to estimate both functional and constant coefficients. The consistency and asymptotic normality of these proposed estimators are established. Moreover, a generalized F-test is developed to test whether the functional coefficients are of particular parametric forms with some underlying economic intuitions, and furthermore, the limiting distribution of the proposed generalized F-test statistic under the null hypothesis is established. Finally, we illustrate the finite sample performance of our approach with simulations and two real data examples in economics.     

新常态下货币供给对工业经济的非对称冲击——兼论冲击路径的情景设计与分析

货币政策冲击对工业产出和价格的非对称影响，是新常态下把握好货币供给政策的方向、力度和节奏的重要参考依据，有助于提升货币供给政策的针对性、灵活性和前瞻性。局部投影方法在不同区制下，计算工业产出和价格对货币供给冲击的脉冲响应结果表明：货币供给冲击对工业产出的影响具有不确定性，且总体上表现为中性特征；而货币供给冲击对工业价格的影响不仅在不同区制下，而且在新常态前后均表现出显著的差异性和非对称性，总体来说是短期有效，长期中性的。情景设计的分析结果显示新常态下采用增加货币供给的政策来刺激工业经济是不可取的，其作用效果可能出现工业产出停滞不前，工业价格急剧飙升的工业滞胀。因此，需要从工业产业升级，工业技术创新等工业供给侧寻求工业经济新的增长点和动力机制。     

Churning races in Canada: Visible minority response change between 2006 and 2011

Why do most people have stable responses to census race questions, while some do not? Using linked Canadian data, we examine personal, social, and economic characteristics that predict response stability as White or as one of six large visible minority groups, versus a change in response to/from White or to/from another visible minority group. Response change rates in Canada are generally comparable to those in the US, UK, and New Zealand. Likely reflecting the centuries-old hegemony of Whites in these countries, White is the most stable response group in Canada as well as the US, UK, and New Zealand. Multiple-race response groups are among the newest and least stable response groups. Social statuses and experiences (mixed ethnic heritage, immigration status, and exposure to own-group members) are generally more predictive of race response stability and change than economic (income level and change in income) or personal statuses (education, age). This highlights the socially-constructed nature of race group boundaries. Joining and leaving a group are often predicted by the same status/characteristic and in the same direction, hinting that the status/characteristic adds complexity to the race-related experiences of constituents.