Efficiency Bounds for Missing Data Models With Semiparametric Restrictions

This paper shows that the semiparametric efficiency bound for a parameter identified by an unconditional moment restriction with data missing at random (MAR) coincides with that of a particular augmented moment condition problem. The augmented system consists of the inverse probability weighted (IPW) original moment restriction and an additional conditional moment restriction which exhausts all other implications of the MAR assumption. The paper also investigates the value of additional semiparametric restrictions on the conditional expectation function (CEF) of the original moment function given always observed covariates. In the program evaluation context, for example, such restrictions are implied by semiparametric models for the potential outcome CEFs given baseline covariates. The efficiency bound associated with this model is shown to also coincide with that of a particular moment condition problem. Some implications of these results for estimation are briefly discussed.     

定向增发股票解禁期市场反应的实证研究

定向增发作为上市公司募集资金的新渠道,近年来规模迅速扩大。以往对其某时段市场反应的实证研究或是集中于从董事会预案至增发实施这一阶段,或是集中于股改限售股解禁阶段,而对限售股解禁期的研究甚少。采用事件研究法对定向增发股票在解禁期的市场表现进行了描述性统计,并分析了实施定向增发上市公司的股利政策、大股东行为方式以及股市周期对解禁期股价走势的影响,以求对投资者的投资行为给予指导。     

基于AR模型的云南省低碳目标管理研究

发展低碳经济的宗旨是降低单位GDP总量的能源,尤其是化石能源消费量,使得经济的碳强度指标遵循从“量变到质量”逐步下降的自然规律。目标管理的运用需要目标的制订、执行、评价以及预测四个环节的协调。笔者运用AR模型来预测下一年度的低碳目标,通过AR模型确定的2011年的碳强度指标和逐年平均下降法确定的碳强度指标非常接近,说明逐年平均下降法确定的年度指标是可行的、具有挑战性的。通过对比“十一·五”期间实际执行路径和理想执行路径的能源消费量和二氧化碳排放量,表明未按目标管理进行的实际执行路径比理想执行路径多排放了约1244万吨二氧化碳,进一步证明了低碳经济目标管理的重要性。     

“作业成本法”在高校生均培养成本核算中的应用

高等院校扩招后生均培养成本成为学校、政府、学生及家长共同关注的一个热点问题。本文运用"作业成本法"的理论,从高校教育成本内涵出发,结合高校的实际情况,探讨了作业成本法在高校生均培养成本核算中的实施条件、程序和实施中应注意的问题。     

完全市场框架下的中国股票溢酬之谜

由于在现实经济中并不是所有的消费者都持有风险资产,且长期以来我国持股家庭和个人比例仍处于较低水平,因此,利用实际经济中的总消费数据对＂股票溢酬之谜＂进行实证分析存在严重的缺陷。利用我国证券市场加权平均每股收益作为完全市场框架下CCAPM中红利和消费增长的代理变量,对＂股票溢酬之谜＂进行检验发现,相对风险厌恶系数符合前人经验研究,我国市场并不存在＂股票溢酬之谜＂。     

基于移动平均模型对上海未来空气质量的定量评估

对2010年上海世博会相关重大事件活动效应进行估算有着十分重大的现实意义,其中部分问题的提出及解决方案对现实中的环境保护起着指导作用.以时间序列预测法的移动平均模型为依据,在对其进行改造的基础上,用通行的Excel程序包进行统计分析,实现了对世博会环境效应的定量评估.     

A new double sampling control chart for monitoring process mean using auxiliary information

Statistical quality control charts have been widely accepted as a potentially powerful process monitoring tool because of their excellent speed in tracking shifts in the underlying process parameter(s). In recent studies, auxiliary-information-based (AIB) control charts have shown superior run length performances than those constructed without using it. In this paper, a new double sampling (DS) control chart is constructed whose plotting-statistics requires information on the study variable and on any correlated auxiliary variable for efficiently monitoring the process mean, namely AIB DS chart. The AIB DS chart also encompasses the classical DS chart. We discuss in detail the construction, optimal design, run length profiles, and the performance evaluations of the proposed chart. It turns out that the AIB DS chart performs uniformly better than the DS chart when detecting different kinds of shifts in the process mean. It is also more sensitive than the classical synthetic and AIB synthetic charts when detecting a particular shift in the process mean. Moreover, with some realistic beliefs, the proposed chart outperforms the exponentially weighted moving average chart. An illustrative example is also presented to explain the working and implementation of the proposed chart.     

A weighted likelihood ratio test-based chart for monitoring process mean and variability

In this paper, a new single exponentially weighted moving average (EWMA) control chart based on the weighted likelihood ratio test, referred to as the WLRT chart, is proposed for the problem of monitoring the mean and variance of a normally distributed process variable. It is easy to design, fast to compute, and quite effective for diverse cases including the detection of the decrease in variability and individual observation case. The optimal parameters that can be used as a design aid in selecting specific parameter values based on the average run length (ARL) and the sample size are provided. The in-control (IC) and out-of-control (OC) performance properties of the new chart are compared with some other existing EWMA-type charts. Our simulation results show that the IC run length distribution of the proposed chart is similar to that of a geometric distribution, and it provides quite a robust and satisfactory overall performance for detecting a wide range of shifts in the process mean and/or variability.     

Estimation of the integrated volatility using noisy high-frequency data with jumps and endogeneity

In this paper, we investigate a new estimator of the integrated volatility of Itô semimartingales in the presence of both market microstructure noise and jumps when sampling times are endogenous. In the first step, our estimation wipes off the effects of the microstructure noise, and in the second step our estimator shrinks the effects of jumps. We provide consistency of the estimator when the jumps have finite variation and infinite variation and establish a central limit theorem for the estimator in a general endogenous time setting when the jumps only have finite variation. Simulation illustrates the performance of the proposed estimator.     

A new per-field classification method using mixture discriminant analysis

In this study, a new per-field classification method is proposed for supervised classification of remotely sensed multispectral image data of an agricultural area using Gaussian mixture discriminant analysis (MDA). For the proposed per-field classification method, multivariate Gaussian mixture models constructed for control and test fields can have fixed or different number of components and each component can have different or common covariance matrix structure. The discrimination function and the decision rule of this method are established according to the average Bhattacharyya distance and the minimum values of the average Bhattacharyya distances, respectively. The proposed per-field classification method is analyzed for different structures of a covariance matrix with fixed and different number of components. Also, we classify the remotely sensed multispectral image data using the per-pixel classification method based on Gaussian MDA.