Noninformative priors for the between-group variance in the unbalanced one-way random effects model with heterogeneous error variances

For the unbalanced one-way random effects model with heterogeneous error variances, we propose the non-informative priors for the between-group variance and develop the first- and second-order matching priors. It turns out that the second-order matching priors do not exist and the reference prior and Jeffreys prior do not satisfy a first-order matching criterion. We also show that the first-order matching prior meets the frequentist target coverage probabilities much better than the Jeffreys prior and reference prior through simulation study, and the Bayesian credible intervals based on the matching prior and reference prior give shorter intervals than the existing confidence intervals by examples.     

Identifying Systemically Important Banks: A temporal approach for macroprudential policies

Contrary to the general belief, systemic risk does not only regard the risk posed by balance sheet relationships and interdependencies among institutions. It also features a temporal dimension related to the inappropriate responses of financial market participants to changes in risk over time. This paper proposes a method to simultaneously address the cross-sectional and the time dimension in which systemic risk materializes. The method is based on the TOPHITS algorithm. It provides three scores, namely borrowing, lending and time scores: the first two represent the systemic importance of the borrowing and the lending activity associated with each financial institution,while the third represents an empirical Early Warning Signal of the financial crisis. Our findings reveal that the identification of the time score as an indicator for an incoming market distress could be relevant to design macro prudential policies.     

利益相关者视角下的企业社会责任研究——以山东省1400家企业问卷调查为例

企业履行社会责任已经成为90年代以来企业界和理论界的共识,利益相关者理论为企业社会责任研究提供了新的视角和框架,按照利益相关者属性的不同可对其进行分类研究.在对山东省1400家企业的调查研究基础上,提出利益相关者的三维金字塔模型,以企业规模、企业性质和企业生命周期为变量考察三个维度社会责任表现的差异性,为企业的社会责任决策提供新的思路,为政府部门制定提升企业社会责任行为表现的政策提供新的视角和依据.     

会计制度与税务制度差异的研究

由于企业会计制度与税务制度所规范的对象和目的不同,会计核算和税务核算之间始终存在着一定的差异。我国现行的会计制度,在会计核算原则、会计政策及会计核算实务等方面都与现行的税务制度存在着较大的差异,而这些差异很可能会导致国家税源的流失,并容易引起征纳双方的纠纷,造成纳税调整增多,甚至不能正确地确认纳税人权益,也给企业避税创造了机会。因此,应加快我国税收制度的改革,减少会计制度与税务制度的差异,确保会计核算的准确性和企业依法纳税。     

农地抵押融资意愿与最优土地规模

运用2014年陕西、宁夏两省2493户农户的调研数据,采用Probit模型,以农地规模对农户参与农地抵押融资意愿的影响分析为基础,实证分析农户参与农地抵押融资意愿最大化条件下的最优土地规模。研究发现：农地规模对农户参与农地抵押融资意愿呈倒U型影响,高陵、平罗地区农户参与农地抵押融资的最优土地规模分别为12.67亩、56.50亩,分别是当地户均农地规模的2.68倍、3.00倍,农业主导产业差异导致最优土地规模有所不同。总体看来,样本农户的家庭规模、经营类型、往返金融机构的便利程度、金融机构数量对农户参与农地抵押融资意愿有较显著的影响,而分区域来看,各控制变量的影响效应存在较大区别。     

关于国民收入随机模型的广义最小方差控制

基于Astrom随机动态模型,讨论了非最小相位经济系统的控制问题.应用广义最小方差控制方法设计出自发投资的调控策略.通过实例表明,国民收入在控制策略作用下波动幅度最小,这是促进经济增长良性循环的重要前提.     

成都市农村居民现金收入影响因素实证分析

当前,面对国内外经济形势的新变化,中央政府把扩大内需作为改变我国经济增长方式和缓解国际压力的重要途径之一,而扩大内需就要提高农村居民的收入水平.为研究如何提高农村居民收入问题,从现金收入的角度,选取相关变量建立向量自回归模型,运用脉冲响应、方差分解等方法,对影响现金收入增速的因素进行了实证分析.结果发现：人均现金收入增速主要受人均工资性收入增速的影响,人均家庭收入增速次之,人均转移性和财产性收入增速最小.     

人口流动对我国居民收入差距影响的回归分解研究

使用"2005年全国1%人口抽样调查数据"和"中国住户收支调查数据"数据库,通过回归分解技术探讨了在控制年龄、性别、地区、受教育程度、行业等变量的前提下,劳动力人口流动对我国居民收入差距的影响。实证分析表明,忽视劳动力人口流动因素,将高估城镇居民收入水平和低估农村居民收入水平,从而夸大我国城乡居民收入差距,造成"推动城镇化就可以显著缩小居民收入差距"的政策误判。分析表明,要缩小我国居民收入差距,更应该把眼光放在如何缩小受教育度差异、地区差异和行业差异等相关措施上。     

农业保险排斥对农民收入的影响：基于倾向值匹配的异质性研究

我国存在较严重的农业保险排斥,对农民收入产生了一定影响。采用2015-2016年北方6省12县种植业保险的调研数据,利用倾向得分匹配法纠正可能存在的样本选择性偏误,系统考察了农业保险排斥对农民收入的影响。实证结果表明：农业保险排斥对农民收入有显著负向影响,受排斥农户与未受排斥农户的农业收入差异为6%左右。考虑异质性后的结果进一步表明,未受排斥的年轻农户和规模扩大型农户农业收入增长明显。此外,相较于高受灾农户,未受排斥的低受灾农户因为受灾和补偿概率较低,农业保费成为农业额外成本从而降低了收入。提出应持续推进政策性农业保险“提标、扩面、增品”,降低农村农业保险排斥,充分发挥农业保险在防风险、稳收入中的作用。     

ESTIMATING A PARAMETER WHEN IT IS KNOWN THAT THE PARAMETER EXCEEDS A GIVEN VALUE

In some statistical problems a degree of explicit, prior information is available about the value taken by the parameter of interest, θ say, although the information is much less than would be needed to place a prior density on the parameter's distribution. Often the prior information takes the form of a simple bound, ‘θ > θ₁ ’ or ‘θ < θ₁ ’, where θ₁ is determined by physical considerations or mathematical theory, such as positivity of a variance. A conventional approach to accommodating the requirement that θ > θ₁ is to replace an estimator, , of θ by the maximum of and θ₁. However, this technique is generally inadequate. For one thing, it does not respect the strictness of the inequality θ > θ₁ , which can be critical in interpreting results. For another, it produces an estimator that does not respond in a natural way to perturbations of the data. In this paper we suggest an alternative approach, in which bootstrap aggregation, or bagging, is used to overcome these difficulties. Bagging gives estimators that, when subjected to the constraint θ > θ₁ , strictly exceed θ₁ except in extreme settings in which the empirical evidence strongly contradicts the constraint. Bagging also reduces estimator variability in the important case for which is close to θ₁, and more generally produces estimators that respect the constraint in a smooth, realistic fashion.