Asymptotic behaviour of regression pre-test estimators with minimal Bayes risk

Pre-test estimators (PTE) are considered which are optimal under a Bayes risk among PTE with general measurable sets as “regions of significance” for the test statistic t associated with the estimate of a given regression coefficient. Asymptotic and some finite sample results are stated and numerical experiments are commented on.     

地方政府怎样管好农村信用社--以贵州为例

农信社交由地方政府管理是落实管理责任市场化及强调地区性的结果。我们在总结贵州试点经验基础上提出建议.改革走向纵深，要解决增资扩股目的短期化、投资股比重低等问题；发展地看待省联社制度设计缺陷；坚持政企分开原则，加强政府对经济活动的外部组织协调，搞好促生产与抗风险的制度安排，帮助农信社形成良好的发展条件。     

基于高频数据的深圳基金市场日内周期性研究

本文利用深市基金指数高频数据,采用Anderson和Bollerslev(1997)提出的弹性傅立叶回归(FlexibleFourierFormregression,即FFF回归)方法首次对深市基金市场进行了日内周期性的研究。通过对高频收益的定性分析,发现基金市场具有同股票市场相似的周期性,并对这一周期性进行了初步的理论解释。通过FFF方法,将该周期因子进行滤波处理以后,基金指数高频绝对收益不再具有明显周期性。FFF回归能较好地确定日内周期因子。     

Do investors in Green Bond market pay a premium? Global evidence

We examine the pricing difference of Green Bonds (GB) and conventional bonds (CBs) in capital markets worldwide. Credit spread is used to observe whether investors would like to pay a premium for GBs over CBs. This study uses panel data regression with hybrid model to analyse daily observations over the period 2016 to 2017. We employ Option-Adjusted spread (OAS) to measure the credit spreads of bonds while controlling for bond specific, macroeconomic and global factors that influence the spread. With the hybrid model used in the panel data analysis, we were able to capture the fixed-effects of variables in a random effect model. We find that GBs are traded at a premium of 63 basis points (BPS), compared with a comparable corporate bond issue. We find that the green label provides issuers an incentive to raise funds through issuing GBs while providing investors an opportunity to diversify their investments returns. Our findings provide several implications to the major stakeholders driving the GB market to scale up the market to finance the required level of global green investment needs. We stress an urgent need to support the growth of the GB market to achieve sustainable development through mitigating climate change challenges.

Abbreviation GB: Green Bond; CB: Conventional Bond; YS: Yield Spread; BPS: Basis Points; OAS: Option-Adjusted Spread; PCSE: Panels Corrected Standard Errors; CPI: Consumer Price Index; GBPs: Green Bond Principles; CBS: Climate Bond Standard     

考虑违约距离的上市公司危机预警模型研究

在上市公司财务危机预警中,违约距离起着重要的不可替代的作用,仅考虑财务指标并不足以完全解释企业财务危机发生的原因.文章应用Merton模型对2002～2004年ST公司和相应配对公司的危机发生之前的违约距离进行了研究,发现危机公司违约距离在危机发生前第三年明显低于正常公司的相应值,违约距离比传统财务指标能更早地预警到企业财务危机的发生.结合违约距离因素的危机预警模型能更明显地提高模型的危机判断正确率.     

宏观投资的影响因素与实证分析

在界定宏观投资的基础上，选择储蓄、价格、利润和利率等影响因素的代表变量，建立固定资产投资的理论模型；以1985－1999年为样本区间，采用SPSS统计分析软件包，对变量数据进行相关性分析和主成分分析，构建我国固定资产投资的回归模型；通过实证分析得出我国固定资产投资各影响因素的结论并给出相应的政策含义。     

基于主成分分析的多元线性回归用水量模型研究——以安徽省为例

用水量的准确预测对水资源规划具有重要意义。根据统计资料,选取了八项主要影响用水量变化的指标。按照主成分分析法原理,运用少数综合因子对影响用水量变化的原有八项指标进行了综合和简化,并研究其贡献率及综合效应;分析了用水量增长的特点,选用Logistic方程对综合因子进行回归分析;结合多元线性回归方法,建立了安徽省用水量模型;拟合结果表明,模型具有较高的精度;运用该模型,对安徽省2010年的用水量进行了预测;为水资源规划与管理提供参考依据。     

ML– and semiparametric estimation in logistic models with incomplete covariate data

ML–estimation of regression parameters with incomplete covariate information usually requires a distributional assumption regarding the concerned covariates that implies a source of misspecification. Semiparametric procedures avoid such assumptions at the expense of efficiency. In this paper a simulation study with small sample size is carried out to get an idea of the performance of the ML–estimator under misspecification and to compare it with the semiparametric procedures when the former is based on a correct assumption. The results show that there is only a little gain by correct parametric assumptions, which does not justify the possibly large bias when the assumptions are not met. Additionally, a simple modification of the complete case estimator appears to be nearly semiparametric efficient.     

论以粮换林换草与河西地区的可持续发展--以武威地区为例

分析了武威地区可持续发展的主要生态环境问题,讨论了生态恢复与重建面临的困境,论证了以粮换林换草对促进区域可持续发展的必要性和有效性,指出了以粮换林换草中应进一步研究的问题,提出生态重建要坚持多措施并举。     

An effective going concern prediction model for the sustainability of enterprises and capital market development

The purpose of this study is to construct a two-stage effective and innovative going concern prediction model to predict going concern doubt for the sustainability of enterprises and capital market development. Samples of this study are the companies listed on the Taiwan Stock Exchange or the Taipei Exchange, totalling 196 companies and including 49 companies with going concern doubt and 147 normal companies (with no going concern doubt). The data are taken from the Taiwan Economic Journal (TEJ) and the Market Observation Post System during the period from 2001 to 2016 (totalling 16 years). This study adopts a two-stage way to construct the going concern prediction models. In Stage I, the traditional statistical method of stepwise regression (SR) and the data mining technique artificial neural network (ANN) are applied to select the important variables. In Stage II, two decision tree algorithms (data mining techniques): classification and regression tree (CART) and C5.0 are used to establish the prediction models. The results show that the SR + CART model has the highest going concern prediction accuracy, with an overall accuracy of 87.42%.