Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.     

Efficient Estimation in Heteroscedastic Partially Linear Varying Coefficient Models

This article considers statistical inference for the heteroscedastic partially linear varying coefficient models. We construct an efficient estimator for the parametric component by applying the weighted profile least-squares approach, and show that it is semiparametrically efficient in the sense that the inverse of the asymptotic variance of the estimator reaches the semiparametric efficiency bound. Simulation studies are conducted to illustrate the performance of the proposed method.     

Conditional time-dependent nonparametric estimators with an application to healthcare production function

ABSTRACT

By using the probabilistic framework of production efficiency, the paper develops time-dependent conditional efficiency estimators performing a non-parametric frontier analysis. Specifically, by applying both full and quantile (robust) time-dependent conditional estimators, it models the dynamic effect of health expenditure on countries’ technological change and technological catch-up levels. The results from the application reveal that the effect of per capita health expenditure on countries’ technological change and technological catch-up is nonlinear and is subject to countries’ specific income levels.     

Estimation of the marginal expected shortfall under asymptotic independence

We study the asymptotic behavior of the marginal expected shortfall when the two random variables are asymptotic independent but positively associated, which is modeled by the so-called tail dependent coefficient. We construct an estimator of the marginal expected shortfall, which is shown to be asymptotically normal. The finite sample performance of the estimator is investigated in a small simulation study. The method is also applied to estimate the expected amount of rainfall at a weather station given that there is a once every 100 years rainfall at another weather station nearby.     

Beyond tail median and conditional tail expectation: Extreme risk estimation using tail Lp-optimization

The conditional tail expectation (CTE) is an indicator of tail behavior that takes into account both the frequency and magnitude of a tail event. However, the asymptotic normality of its empirical estimator requires that the underlying distribution possess a finite variance; this can be a strong restriction in actuarial and financial applications. A valuable alternative is the median shortfall (MS), although it only gives information about the frequency of a tail event. We construct a class of tail L^p-medians encompassing the MS and CTE. For p in (1,2), a tail L^p-median depends on both the frequency and magnitude of tail events, and its empirical estimator is, within the range of the data, asymptotically normal under a condition weaker than a finite variance. We extrapolate this estimator and another technique to extreme levels using the heavy-tailed framework. The estimators are showcased on a simulation study and on real fire insurance data.     

试论强化国家创新体系

我国现有的国家创新系统存在效率低下和创新活力不强的问题。强化我国的创新工作应该采取适应市场 ,及时调整创新系统中各因素的角色 ;实现创新组织网络化达到资源共享 ;政府推动创新文化建设 ,营造创新氛围这三项措施 ,从而提高我国整体创新能力和国家创新系统效率。     

土地利用管制制度效率探讨

对于公共管理,管制是重要的管理手段。文章用经济学的观点阐述管制政策的效率原理,并在此基础上分析我国土地利用管制政策的效率,最后提出我国管制政策改进的建议和实施的条件。     

绍兴市贷款产出效率分析

通过对1991-2004年绍兴市实现的GDP与占用贷款变动关系的统计分析,指出贷款产出效率持续走低存在的问题,在分析了贷款产出效率下降的主要原因后,提出了相应的对策建议.     

高校德育低效的内因分析

高校德育低效除社会大环境的负面影响外,高校德育自身的问题也不可忽视。智德分离;不尊重学生的主体地位;忽视培育和发展学生的情感、忽视学生的差异和个性;德育与学生的生活和需要脱节;育德者的人格不适应德育的要求等德育自身的缺陷是德育低效的内在原因。     

公平与效率关系的再思考

公平已成为当代中国的热点问题。市场经济是科学认识公平与效率关系的逻辑起点。而市场扭曲和政府错位是目前社会公平陷于困境的主要原因。只有当政府和企业各司其职 ,也就是政府致力于公平 ,企业专注于效率时 ,公平和效率的冲突才能得到有效缓解