金融结构对产业结构影响的时变特征研究——基于SUR和TVPSS模型的实证检验

金融结构与产业结构的关系一直是学术界的研究热点.文章利用中国1998—2017年的年度数据,构建似不相关回归模型从金融结构规模、效率及深化的角度分析金融结构对产业结构合理化和高级化的影响,建立时变参数状态空间模型描绘了金融结构对产业结构合理化和高级化的动态冲击.实证分析结果表明:金融结构规模提高产业结构合理化水平,促进了产业结构高级化;金融结构效率提高产业结构合理化水平,抑制了产业结构高级化;金融结构深化降低产业结构合理化水平,促进了产业结构高级化.金融结构规模、金融结构效率及金融结构深化对产业结构合理化和高级化的冲击均呈现出时变特征;金融结构对产业结构合理化的影响滞后于其对产业结构高级化的影响.金融结构对产业结构的冲击波幅呈现出前期波动大、后期较为平缓的状态,部分金融结构变量对产业结构的动态冲击呈现出"长尾"现象.当前的中国金融结构已经不适合当前的产业结构,需调整金融结构,以提升产业结构合理化水平和高级化水平.     

属性集容量确定的夹挤式测度模式及其推算模型

迄今，为解决多属性偏好关联决策属性集容量判断指数复杂性难题所提出的λ模糊测度模式与k-可加模糊测度模式，以及建构在它们之上的属性集容量确定的推算模型，尚存在着适用性差的技术不足。为此，以平衡容量判断的可操作性和容量推算的准确性为视角，提出了一种新容量测度模式，即关于容量判断与推算的夹挤式测度模式，并在此基础上通过引入决策者较易判断给出的容量序信息构建了相应的容量推算模型。基于数值模拟的对比分析表明：新模式不仅在应用可行性上高于k-可加模糊测度模式，而且从容量推算的准确性上看也明显优于λ模糊测度模式和k-可加模糊测度模式，因而对实际决策具有更强的适用性。     

The exponentiated power exponential regression model with different regression structures: application in nursing data

We define the exponentiated power exponential distribution and propose a regression model with different systematic structures based on the new distribution. We show that the new regression model can be applied to dispersion data since it represents a parametric family of models that includes as sub-models some widely-known regression models. It then can be used more effectively in the analysis of real data. We use maximum likelihood estimation and derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. Some global-influence measurements are also investigated and simulation studies are performed to evaluate the accuracy of the estimates. We provide an application of the regression model with four systematic structures to nursing activities score data in the Unit of the Medical Clinic of University of São Paulo (USP) Hospital.     

Cox regression of clustered event times with covariates missing not at random

Motivated by a recent tuberculosis (TB) study, this paper is concerned with covariates missing not at random (MNAR) and models the potential intracluster correlation by a frailty. We consider the regression analysis of right‐censored event times from clustered subjects under a Cox proportional hazards frailty model and present the semiparametric maximum likelihood estimator (SPMLE) of the model parameters. An easy‐to‐implement pseudo‐SPMLE is then proposed to accommodate more realistic situations using readily available supplementary information on the missing covariates. Algorithms are provided to compute the estimators and their consistent variance estimators. We demonstrate that both the SPMLE and the pseudo‐SPMLE are consistent and asymptotically normal by the arguments based on the theory of modern empirical processes. The proposed approach is examined numerically via simulation and illustrated with an analysis of the motivating TB study data.     

Measuring and Achieving Equity in Multiperiod Emergency Material Allocation

Emergency material allocation is an important part of postdisaster emergency logistics that is significant for improving rescue effectiveness and reducing disaster losses. However, the traditional single‐period allocation model often causes local surpluses or shortages and high cost, and prevents the system from achieving an equitable or optimal multiperiod allocation. To achieve equitable allocation of emergency materials in the case of serious shortages relative to the demand by victims, this article introduces a multiperiod model for allocation of emergency materials to multiple affected locations (using an exponential utility function to reflect the disutility loss due to material shortfalls), and illustrates the relationship between equity of allocations and the cost of emergency response. Finally, numerical examples are presented to demonstrate both the feasibility and the usefulness of the proposed model for achieving multiperiod equitable allocation of emergency material among multiple disaster locations. The results indicate that the introduction of a nonlinear utility function to reflect the disutility of large shortfalls can make the material allocation fairer, and minimize large losses due to shortfalls. We found that achieving equity has a significant but not unreasonable impact on emergency costs. We also illustrate that using differing utility functions for different types of materials adds an important dimension of flexibility.     

学术论文一稿多投的"诊"与"治"

一稿多投是学术界由来已久且难以医治的顽症。究其原因,主要是科研压力之下作者的投机心态、作者投稿经验的匮乏、稿件时效性的压力以及网上投稿的便捷与低成本等因素使然。一稿多投会造成编方人力资源的浪费,容易引发一稿多发,挤占宝贵的版面资源,引起版权纠纷,破坏学术诚信,扰乱科研秩序。要从加强制度建设、加大惩罚力度、利用高科技进行防范、健全监督机制、加强学术道德教育、提高编辑效率等方面,把"他律"与"自律"结合起来,惩防并举,标本兼治,多管齐下,齐抓共管,从根本上规范科研活动,根治一稿多投顽症。     

Probabilistic frontier regression models for binary type output data

This paper proposes a probabilistic frontier regression model for binary type output data in a production process setup. We consider one of the two categories of outputs as ‘selected’ category and the reduction in probability of falling in this category is attributed to the reduction in technical efficiency (TE) of the decision-making unit. An efficiency measure is proposed to determine the deviations of individual units from the probabilistic frontier. Simulation results show that the average estimated TE component is close to its true value. An application of the proposed method to the data related to the Indian public sector banking system is provided where the output variable is the indicator of level of non-performing assets. Individual TE is obtained for each of the banks under consideration. Among the public sector banks, Andhra bank is found to be the most efficient, whereas the United Bank of India is the least.     

Oracle GMM estimation for misspecified models via thresholding

We propose a thresholding generalized method of moments (GMM) estimator for misspecified time series moment condition models. This estimator has the following oracle property: its asymptotic behavior is the same as of any efficient GMM estimator obtained under the a priori information that the true model were known. We propose data adaptive selection methods for thresholding parameter using multiple testing procedures. We determine the limiting null distributions of classical parameter tests and show the consistency of the corresponding block-bootstrap tests used in conjunction with thresholding GMM inference. We present the results of a simulation study for a misspecified instrumental variable regression model and for a vector autoregressive model with measurement error. We illustrate an application of the proposed methodology to data analysis of a real-world dataset.     

Robust block bootstrap panel predictability tests*

This article develops two block bootstrap-based panel predictability test procedures that are valid under very general conditions. Some of the allowable features include cross-sectional dependence, heterogeneous predictive slopes, persistent predictors, and complex error dynamics, including cross-unit endogeneity. While the first test procedure tests if there is any predictability at all, the second procedure determines the units for which predictability holds in case of a rejection by the first. A weak unit root framework is adopted to allow persistent predictors, and a novel theory is developed to establish asymptotic validity of the proposed bootstrap. Simulations are used to evaluate the performance of our tests in small samples, and their implementation is illustrated through an empirical application to stock returns.