复杂性与创造性思维

经典科学思维方式和近代认识论都受到了复杂性的严峻挑战。如何认识复杂性,已成为当代科学和哲学的共同任务。从科学的视角来看,必须运用复杂性思维认识复杂性,建立复杂性科学;从哲学的视角来看,则应通过创造性思维来推动认识论变革。     

“V一下”结构语义分析

通过较为全面的观察及较为细致的分析鉴别,我们最终确定“V一下”的结构语义主要有二：一是动作的具体数量;二是动作的少量。其语用功能为“减小对对方的强加”。     

Measuring Nonlinear Granger Causality in Mean

We propose model-free measures for Granger causality in mean between random variables. Unlike the existing measures, ours are able to detect and quantify nonlinear causal effects. The new measures are based on nonparametric regressions and defined as logarithmic functions of restricted and unrestricted mean square forecast errors. They are easily and consistently estimated by replacing the unknown mean square forecast errors by their nonparametric kernel estimates. We derive the asymptotic normality of nonparametric estimator of causality measures, which we use to build tests for their statistical significance. We establish the validity of smoothed local bootstrap that one can use in finite sample settings to perform statistical tests. Monte Carlo simulations reveal that the proposed test has good finite sample size and power properties for a variety of data-generating processes and different sample sizes. Finally, the empirical importance of measuring nonlinear causality in mean is also illustrated. We quantify the degree of nonlinear predictability of equity risk premium using variance risk premium. Our empirical results show that the variance risk premium is a very good predictor of risk premium at horizons less than 6 months. We also find that there is a high degree of predictability at the 1-month horizon, that can be attributed to a nonlinear causal effect. Supplementary materials for this article are available online.     

论暑期社会实践与专业实践教学的衔接

不应单纯将暑期社会实践当作一项思想政治教育活动,而应将之同时作为实践教学的重要环节和学生实践能力培养的重要举措。可以开展专题调研、挂职锻炼、管理咨询等与专业能力密切相关的多样化的实践形式。为保证实践效果,需实现暑期实践活动的课程化、机制化和基地化。     

Beta系数跨期时变与公司估值

从理论上剖析Beta系数跨期时变、时间要素设定差异对系统性风险度量及公司估值结果的影响,并以2005年1月1日至2014年12月31日为样本周期,以有色、钢铁、石化、房地产、银行等5个周期性行业板块收益率及市场平均收益率的周数据和月数据为研究样本,对理论分析结论进行实证检验。研究发现：（1）时间要素设定差异会显著影响Beta系数稳定性;（2）时间要素设定差异对系统性风险度量及公司估值结果影响显著;（3）审慎设定时间要素,有利于提高Beta系数稳定性,同时降低系统性风险度量及公司估值误差。其中,“5～10年”是更为可取的Beta系数估计时段,并应优先选择以“周”为单位的收益率度量时限,其次是以“月”为单位。     

Hierarchical Bayes estimation of spatial statistics for rates

The U.S. Bureau of Labour Statistics publishes monthly unemployment rate estimates for its 50 states, the District of Columbia, and all counties, under Current Population Survey. However, the unemployment rate estimates for some states are unreliable due to low sample sizes in these states. Datta et al. (1999) proposed a hierarchical Bayes (HB) method using a time series generalization of a widely used cross-sectional model in small-area estimation. However, the geographical variation is also likely to be important. To have an efficient model, a comprehensive mixed normal model that accounts for the spatial and temporal effects is considered. A HB approach using Markov chain Monte Carlo is used for the analysis of the U.S. state-level unemployment rate estimates for January 2004-December 2007. The sensitivity of such type of analysis to prior assumptions in the Gaussian context is also studied.     

Zero-inflated Poisson and negative binomial integer-valued GARCH models

Zero inflation means that the proportion of 0's of a model is greater than the proportion of 0's of the corresponding Poisson model, which is a common phenomenon in count data. To model the zero-inflated characteristic of time series of counts, we propose zero-inflated Poisson and negative binomial INGARCH models, which are useful and flexible generalizations of the Poisson and negative binomial INGARCH models, respectively. The stationarity conditions and the autocorrelation function are given. Based on the EM algorithm, the estimating procedure is simple and easy to be implemented. A simulation study shows that the estimation method is accurate and reliable as long as the sample size is reasonably large. A real data example leads to superior performance of the proposed models compared with other competitive models in the literature.     

Asymptotic theory for fractionally integrated asymmetric power ARCH models

Consistency and asymptotic normality of quasi-maximum likelihood estimators (QMLEs) for the fractionally integrated asymmetric power ARCH (FIAPARCH) process are proved. The moment conditions are assumed only for standardized errors. We show the properties for a wide range of QMLEs including Gaussian QMLE.     

Complexity leadership in bureaucratic forms of organizing: A meso model

We consider Complexity Leadership Theory [Uhl-Bien, M., Marion, R., & McKelvey, B. (2007). Complexity Leadership Theory: Shifting leadership from the industrial age to the knowledge era. The Leadership Quarterly.] in contexts of bureaucratic forms of organizing to describe how adaptive dynamics can work in combination with administrative functions to generate emergence and change in organizations. Complexity leadership approaches are consistent with the central assertion of the meso argument that leadership is multi-level, processual, contextual, and interactive. In this paper we focus on the adaptive function, an interactive process between adaptive leadership (an agentic behavior) and complexity dynamics (non-agentic social dynamics) that generates emergent outcomes (e.g., innovation, learning, adaptability) for the firm. Propositions regarding the actions of complexity leadership in bureaucratic forms of organizing are offered.     

Short- and long-term stability in organizational networks: Temporal structures of project teams

Network research focuses on patterns of stable relationships, where stability represents the unfolding of social processes over long time frames. We argue that social interactions exhibit important regularities in different time frames (short and long term), reflecting distinct social processes. We illustrate the value of this distinction through a comparative case study of technology-mediated communication, within two project teams in a digital marketing agency. We examine how the embedding of interpersonal interactions in processes of reciprocity and closure over different time horizons enables the emergence of cohesion in the face of constant compositional changes. We propose that the time frames in which stable patterns of interactions develop are the key to understanding the nature of the underlying social processes with short-term patterns of closure and reciprocity representing adaptation to change while longer term patterns indicate cohesion. Our results are supportive of this argument and show that the two teams exhibit the same regularities in interactions but across different time horizons. We discuss the implication of our findings and argue that distinguishing between short- and long-term stability of social networks offers a novel and promising avenue for network research.