企业核心刚性进化机制研究:基于CAS视角

根据复杂自适应系统理论，本文对组织的性质进行了再认识。在此基础上，提出企业核心刚性进化的动力和条件分别是企业系统中合法系统与影子系统之间的相互作用．以及运行区域的五个控制参数，即信息流动速率、焦虑抑制水平、成员多样性程度、联系水平、权力差异程度。进一步，分析了随着控制参数的变化，企业进入不同的运行区域，核心刚性如何实现螺旋式进化的机制。     

国内上市公司债务融资效率的实证分析

经典公司理论表明,作为硬约束,债务融资能够在一定程度上降低公司内在的代理成本,从而提高公司经营效率。文章利用上海证券交易所A股上市的522家上市公司2003-2008年的财务报表相关数据对此做一个实证研究。时间固定效应模型回归的结果显示,我国上市公司的资产收益率与债务融资率之间呈负相关关系,国内上市公司的债务融资并不能有效控制经营者为最大化自身收益而采取的机会主义行为,代理成本的存在一定程度上降低了公司经营效率。     

银行危机宏观影响因素变迁的实证研究

本文选择了108个国家在1980-2009年的样本数据,采用随机系数Logit模型,研究了宏观因素冲击对于银行危机的影响作用。研究结果表明,各种宏观因素的影响程度在不同经济发展水平国家的银行危机中具有明显的差异;银行体系的资产质量和流动性风险暴露是银行危机产生的最主要原因;且随着金融自由化和国际化的发展,银行危机的发生原因也逐渐发生变化。因此,与时俱进地采取某些监管措施,例如限制银行贷款增速、监控银行跨境资本流动、控制外债规模等,有助于降低银行危机发生的概率。     

用PERT法求解不确定网络中活动时间参数的改进研究

基于计划评审技术(PERT)中β分布的假设,阐述了常规三时估计方法的不足,提出了相应解决对策,即采用限定概率三时估计法分别建立了β分布和Gamma分布拟合方差最小模型,用微粒群算法求解模型,并且将求解结果与常规三时估计方法进行了对比研究。实例证实,对于不同类型的活动,可以选择合理的分布类型并利用限定概率三时估计法,对各个活动的持续时间服从的分布密度函数进行拟合,从而提高PERT的精度和效率。     

Using the theory of planned behavior to predict food safety behavioral intention: A systematic review and meta-analysis

Approximately 61% of foodborne outbreaks in the United States are traced to food handler behavior. The literature suggests that food safety knowledge does not always translate into improved behaviors. Thus, multiple researchers have recommended an examination of the gap between knowledge and behavioral intentions. The purpose of this meta-analysis was to evaluate the ability of the Theory of Planned Behavior to predict food safety intentions.A total of 1550 studies were screened against the inclusion criteria, leaving 46 study records. The overall random effect size (r) was 0.282 (p < 0.001), indicated that the Theory of Planned Behavior constructs significantly predicted food safety behavioral intention. Subjective norms were identified as the most influential variable. The between-study heterogeneity ratio was low and non-significant, suggesting that individual food safety intentions were very similar in various service environments. Studies with a large sample tended to have significantly stronger individual norms to intention relationships. Stronger individual norms created a correspondent group culture that encouraged others to react or conform to the norm. With low true effect variances, a considerable amount of the variance is still subject to future research.     

The effect of China's open-door tourism policy on Taiwan: Promoting or suppressing tourism from other countries to Taiwan?

This study employs an extended gravity model to analyse the complementarity or competitiveness relationship of the number of inbound tourists and corresponding tourism revenue between China and 19 other nations under the implementation of China's Open-door Tourism Policy to Taiwan in 2008. A simulation for 2018–2021 demonstrates the sustained impact of this policy. The results show that the number of tourists to Taiwan from China reached its peak in 2015 at 41% and will decrease to 9% by 2021. The corresponding tourism revenue will decrease from 49% to 11% over the same period. The results also show that if the number of tourists from China remains above 836,772, the number of tourists from Japan, Hong Kong, Australasia, North America, and Europe will still increase. However, the number of tourists from South Korea and South and Southeast Asia will increase continuously regardless of tourists from China, even far below 836,772.     

Forecasting economic time series using score-driven dynamic models with mixed-data sampling

We introduce a mixed-frequency score-driven dynamic model for multiple time series where the score contributions from high-frequency variables are transformed by means of a mixed-data sampling weighting scheme. The resulting dynamic model delivers a flexible and easy-to-implement framework for the forecasting of low-frequency time series variables through the use of timely information from high-frequency variables. We verify the in-sample and out-of-sample performances of the model in an empirical study on the forecasting of U.S. headline inflation and GDP growth. In particular, we forecast monthly headline inflation using daily oil prices and quarterly GDP growth using a measure of financial risk. The forecasting results and other findings are promising. Our proposed score-driven dynamic model with mixed-data sampling weighting outperforms competing models in terms of both point and density forecasts.     

Hierarchical GARCH

There is strong empirical evidence that the GARCH estimates obtained from panels of financial time series cluster. In order to capture this empirical regularity, this paper introduces the Hierarchical GARCH (HG) model. The HG is a nonlinear panel specification in which the coefficients of each series are modeled as a function of observed series characteristic and an unobserved random effect. A joint panel estimation strategy is proposed to carry out inference for the model. A simulation study shows that when there is a strong degree of coefficient clustering panel estimation leads to substantial accuracy gains in comparison to estimating each GARCH individually. The HG is applied to a panel of U.S. financial institutions in the 2007–2009 crisis, using firm size and leverage as characteristics. Results show evidence of coefficient clustering and that the characteristics capture a significant portion of cross sectional heterogeneity. An out-of-sample volatility forecasting application shows that when the sample size is modest coefficient estimates based on the panel estimation approach perform better than the ones based on individual estimation.     

Random assignments of bundles

We propose a model studying the random assignments of bundles with no free disposal. The key difference between our model and the one where objects are allocated (see Bogomolnaia and Moulin (2001)) is one of feasibility. The implications of this difference are significant. Firstly, the characterization of sd-efficient random assignments is more complex. Secondly, we are able to identify a preference restriction, called essential monotonicity, under which the random serial dictatorship rule (extended to the setting with bundles) is equivalent to the probabilistic serial rule (extended to the setting with bundles). This equivalence implies the existence of a rule on this restricted domain satisfying sd-efficiency, sd-strategy-proofness, and equal treatment of equals. Moreover, this rule only selects random assignments which can be decomposed as convex combinations of deterministic assignments.     

Queue behavioural patterns for passengers at airport terminals: A machine learning approach

Passengers go through different handling processes inside airport terminal buildings. The quality of these processes is usually measured by the time passengers require and by the level of comfort experienced by them. We present an analysis of behavioural patterns in queues at check-in desks and security controls, which are two of the most critical processes regarding passenger service. The passengers' flow is simulated to obtain queue lengths at one busy European airport between 2014 and 2016, supported by real flight data. Simulation is designed as a store-and forward cell-based system, whose parameters have been tuned and validated with real data from observations and empirical capacity and demand studies within the airport. Random Forest algorithms are then implemented to develop different models for each parameter prediction, after a data analysis stage based on statistical and visualization methods. Feature analysis techniques between dependent variables and the target outputs (queue lengths) determine which are the fundamental elements to explain queue behaviour and to predict target variables. We provide a method to forecast behavioural patterns at check-in desks and security controls, to help airport operators to implement adequate response policies. Queue behavioural patterns are captured by Machine Learning models, which can be used to offer improved passenger services (such as real-time predictions for expected waiting time at queues), or can be considered in a dynamic approach for terminal services design (as the entire progress of terminal handling depends on the stochastic behaviour of passengers). This could be a key tool for managing passengers demand and optimise the infrastructure's capacity through resource allocation.