基于DEA模型的安徽省区域创新效率研究

以安徽省16个地级市的数据为依据,对不同城市的创新效率进行了实证研究。首先测度了2012年安徽省16个地级市区域创新的综合效率、技术效率和规模效率,并把安徽省分为皖北、皖中和皖南3个区域进行创新效率对比研究,最后根据DEA模型的优化目标值来探讨提高各地级市的区域创新效率的途径。     

政治关联与公司治理有效性——基于上市公司证券违规的数据

管理者政治关联在强化企业竞争优势的同时,也会弱化公司治理机制的有效性.本文基于公司违规行为发生及管理者被迫离职两个方面,利用Bivariate Probit模型和Logit模型研究管理者政治关联对公司治理有效性的影响.研究发现,拥有政治关联的管理者所在企业更加倾向发生违规行为,尤其是民营企业;管理者的政治关联能够降低其违规行为被稽查处理的可能性,尤其是国有企业.政治关联有助于管理者建立职位壕沟,降低管理者被迫离职的概率;而企业违规行为被稽查后,管理者被迫离职的可能性将显著提高.文章的研究结果证明,管理者政治关联会弱化企业发生违规行为的事前监管,同时也会降低企业违规行为的稽查在事后监管中的公司治理效果.     

我国上市公司不当关联交易浅析

企业之间公允的关联交易能够降低交易成本,优化资本结构,增加企业价值.但在实际操作中,一些企业往往利用不当关联交易转移利润,套取资本市场资金,损害投资者和债权人利益,扰乱资本市场秩序.本文主要分析了我国上市公司不当关联交易的主要表现形式及其产生的原因,提出了控制上市公司不当关联交易的措施.     

Trading volume and stock returns: A meta-analysis

I examine 468 estimates on the relationship between trading volume and stock returns reported in 44 studies. I study publication bias together with Bayesian and frequentist model averaging to explain the heterogeneity in the estimates. The results yield three key conclusions. First, publication bias distorts the findings of the primary studies. Second, the predictability of stock returns varies with different markets and stock types. Third, different data characteristics, structural variations and methodologies used drive the heterogeneity in the results of the primary articles. In particular, one should be cautious when using monthly data or VAR models.     

农业创业情境下商业模式设计的影响机制研究——基于模糊集定性比较分析

农业创业者如何通过整合与管理资源与能力，设计新颖型商业模式创造更多价值，是实现乡村振兴战略的关键措施。文章基于资源编排、商业模式理论，以央视《致富经》栏目40个案例为样本，运用模糊集定性比较分析（fsQCA）方法，剖析资源编排、机会能力、创业学习所构成的前因变量组态对农业创业活动中商业模式设计的影响机制。研究发现，资源编排在农业创业活动中发挥不可或缺的关键作用；相较于资源结构化和资源捆绑，资源利用在新颖型商业模式设计中的作用更为突出；机会能力须与资源编排共同作用，方可实现新颖型商业模式设计；在资源编排作用缺乏时，创业学习的发挥有助于新颖型商业模式设计的形成。研究成果诠释了农业创业情境下资源编排、机会能力与创业学习对商业模式设计的影响机制，为开展农业创业活动提供指导。     

An information-theoretic approach for forecasting interval-valued SP500 daily returns

We develop an iterative and efficient information-theoretic estimator for forecasting interval-valued data, and use our estimator to forecast the SP500 returns up to five days ahead using moving windows. Our forecasts are based on 13 years of data. We show that our estimator is superior to its competitors under all of the common criteria that are used to evaluate forecasts of interval data. Our approach differs from other methods that are used to forecast interval data in two major ways. First, rather than applying the more traditional methods that use only certain moments of the intervals in the estimation process, our estimator uses the complete sample information. Second, our method simultaneously selects the model (or models) and infers the model’s parameters. It is an iterative approach that imposes minimal structure and statistical assumptions.     

FX Intervention in the New Keynesian Model

We develop an open-economy New Keynesian Model with foreign exchange (FX) intervention in the presence of a financial accelerator and shocks to risk appetite in international capital markets. We obtain closed-form solutions for optimal monetary and FX intervention policies assuming the central bank cannot commit to future policies, and we compare the solution to that under policy commitment. We show how FX intervention can help reduce the volatility of the exchange rate, of inflation, and of the output gap, thus mitigating welfare losses associated with shocks in the international capital markets. We also show that, when the financial accelerator is strong, there is a risk of indeterminacy (self-fulfilling currency and inflation movements) although FX intervention can reduce this risk and thus reinforce the credibility of the inflation targeting regime. Model simulations match well the impact of a VIX shock obtained by local projections on a panel of inflation targeting emerging markets.     

Objective methods for graphical structural learning

Graphical models are used for expressing conditional independence relationships among variables by the means of graphs, whose structure is typically unknown and must be inferred by the data at hand. We propose a theoretically sound Objective Bayes procedure for graphical model selection. Our method is based on the Expected-Posterior Prior and on the Power-Expected-Posterior Prior. We use as input of the proposed methodology a default improper prior and suggest computationally efficient approximations of Bayes factors and posterior odds. In a variety of simulated scenarios with varying number of nodes and sample sizes, we show that our method is highly competitive with, or better than, current benchmarks. We also discuss an application to protein-signaling data, which wieldy confirms existing results in the scientific literature.     

考虑换乘费用的城市公共交通票价优化

换乘所产生的附加费用,如时间和票价等,导致配流影响因素产生变化,如果沿用传统方式将降低预测精度和可靠性。通过引入换乘次数和方式等因子计算出行等待、乘车、换乘及风险评估预留时间等,定义广义出行费用与计算方法;建立双层规划模型求解最优票价,最后通过算例分析弹性出行需求、换乘费用、票价之间的关系。计算结果表明,换乘费用对出行需求的影响小于票价优化对出行需求的影响,优化票价随换乘费用增加而加速降低,为公共交通票价优化提供研究依据。     

Paired comparison models with age effects modeled as piecewise quadratic splines

We propose new models for analyzing pairwise comparison data, such as that relating to sports. We focus on changes in players’ strengths and the prediction of future results. Our models are based on the Thurstone-Mosteller and Bradley–Terry models, and make use of the time variation in the parameters. Furthermore, we apply our models to data from the Japanese traditional sport sumo, and analyze this data. The proposed models perform better than the standard Thurstone-Mosteller and Bradley–Terry models according to both the Akaike information criterion and the Brier score. We compare the proposed models in detail by focusing on individual sumo wrestlers.