政府性基金征收的正当程序研究 ——基于民航发展基金的个案分析

政府性基金的设立,为解决改革开放初期基础设施投上发挥了政策性作用。然而,随着时代的变迁与市场经济模式的完善,作为时代产物的政府性基金,其合法性、合理性与透明度均存在着质疑。选择以民航发展基金为个案,对民航发展基金的由来与发展、征收的使用和制度规范以及设立程序进行阐述与分析不难发现,民航发展基金在前置程序、立法程序、征收标准法律依据、实施程序等方面存在着正当法律程序的偏差,致使其合法性受到质疑。因此,政府性基金的设立,应该在前置程序、程序立法、实施程序、纠纷解决正当程序诸方面做出制度性安排。     

桥塞坐封过程仿真模拟研究

桥塞作为井下封层技术的关键设备,由于它施工工序少、周期短、卡封位置准确而得到了广泛应用。针对设计的可钻式非金属桥塞坐封过程开展模拟研究,涉及弹塑性材料接触、超弹性材料接触和非金属材料动态断裂的非线性动力学问题,利用有限元法建立该桥塞全尺寸模型和所有部件之间的广义接触关系,通过有限元模拟动态坐封过程获得桥塞各部件运动变形特性、桥塞与套管之间的密封特性和动态坐封力等数据,该桥塞坐封完成时,坐封机构部件轴向位移110 mm,坐封力约45 kN。模拟坐封也为进一步优化该型桥塞的结构和评估坐封性能提供了理论依据,对该型桥塞的推广应用奠定了基础。     

灌装包装生产线效率评估系统的设计

针对国内一部分食品企业灌装包装生产线难以量化生产线效率的现状,提出了一套切实有效的生产线效率评估系统。通过评估设备速度V-曲线,以现代信息化技术为基础,提取出关键设备的停机记录,其中包含了停机的起始时间、终止时间、时长、分类、机台和原因等几大关键项内容,然后对内外部因素造成的各类时间损失进行准确量化评估,从而构建以总资产利用率、线毛产出率、设备综合利用率和线效率4项指标为衡量标准的一套生产线效率评估系统。本系统实现了灌装包装生产线数字化过程,整体透视出企业的综合管理能力、部门之间的协调配合能力、设备运行能力和故障处理能力。基于此数字化效率评估系统,可以分析停机成因,识别出影响生产线效率的核心要素,通过针对性的优化手段,达到提高效率的目的,实现降低成本和提高利润的业务目标。     

Testing Serial Correlation in Partially Linear Additive Errors-in-variables Models

This article considers testing serial correlation in partially linear additive errors-in-variables model. Based on the empirical likelihood based approach, a test statistic was proposed, and it was shown to follow asymptotically a chi-square distribution under the null hypothesis of no serial correlation. Finally, some simulation studies are conducted to illustrate the performance of the proposed method.     

Conditional VAR and Expected Shortfall: A New Functional Approach

We estimate two well-known risk measures, the value-at-risk (VAR) and the expected shortfall, conditionally to a functional variable (i.e., a random variable valued in some semi(pseudo)-metric space). We use nonparametric kernel estimation for constructing estimators of these quantities, under general dependence conditions. Theoretical properties are stated whereas practical aspects are illustrated on simulated data: nonlinear functional and GARCH(1,1) models. Some ideas on bandwidth selection using bootstrap are introduced. Finally, an empirical example is given through data of the S&P 500 time series.     

Understanding Estimators of Treatment Effects in Regression Discontinuity Designs

In this paper, we propose two new estimators of treatment effects in regression discontinuity designs. These estimators can aid understanding of the existing estimators such as the local polynomial estimator and the partially linear estimator. The first estimator is the partially polynomial estimator which extends the partially linear estimator by further incorporating derivative differences of the conditional mean of the outcome on the two sides of the discontinuity point. This estimator is related to the local polynomial estimator by a relocalization effect. Unlike the partially linear estimator, this estimator can achieve the optimal rate of convergence even under broader regularity conditions. The second estimator is an instrumental variable estimator in the fuzzy design. This estimator will reduce to the local polynomial estimator if higher order endogeneities are neglected. We study the asymptotic properties of these two estimators and conduct simulation studies to confirm the theoretical analysis.     

Regularization with Maximum Entropy and Quantum Electrodynamics: The Merg(E) Estimators

It is well-known that under fairly conditions linear regression becomes a powerful statistical tool. In practice, however, some of these conditions are usually not satisfied and regression models become ill-posed, implying that the application of traditional estimation methods may lead to non-unique or highly unstable solutions. Addressing this issue, in this paper a new class of maximum entropy estimators suitable for dealing with ill-posed models, namely for the estimation of regression models with small samples sizes affected by collinearity and outliers, is introduced. The performance of the new estimators is illustrated through several simulation studies.     

Robust Inference for Near-Unit Root Processes with Time-Varying Error Variances

The autoregressive Cauchy estimator uses the sign of the first lag as instrumental variable (IV); under independent and identically distributed (i.i.d.) errors, the resulting IV t-type statistic is known to have a standard normal limiting distribution in the unit root case. With unconditional heteroskedasticity, the ordinary least squares (OLS) t statistic is affected in the unit root case; but the paper shows that, by using some nonlinear transformation behaving asymptotically like the sign as instrument, limiting normality of the IV t-type statistic is maintained when the series to be tested has no deterministic trends. Neither estimation of the so-called variance profile nor bootstrap procedures are required to this end. The Cauchy unit root test has power in the same 1/T neighborhoods as the usual unit root tests, also for a wide range of magnitudes for the initial value. It is furthermore shown to be competitive with other, bootstrap-based, robust tests. When the series exhibit a linear trend, however, the null distribution of the Cauchy test for a unit root becomes nonstandard, reminiscent of the Dickey-Fuller distribution. In this case, inference robust to nonstationary volatility is obtained via the wild bootstrap.     

On asymptotic distribution of prediction in functional linear regression

There has been substantial recent attention on problems involving a functional linear regression model with scalar response. Among them, there have been few works dealing with asymptotic distribution of prediction in functional linear regression models. In recent literature, the centeral limit theorem for prediction has been discussed, but the proof and conditions under which the random bias terms for a fixed predictor converge to zero have been ignored so that the impact of these terms on the convergence of the prediction has not been well understood. Clarifying the proof and conditions under which the bias terms converge to zero, we show that the asymptotic distribution of the prediction is normal. Furthermore, we have derived those results related to other terms that already obtained by others, under milder conditions. Finally, we conduct a simulation study to investigate performance of the asymptotic distribution under various parameter settings.