矩约束模型的最优矩条件选取方法

大量经济、金融以及企业管理等领域研究对象的行为特征可以通过矩约束模型来刻画。然而,该模型中参数的估计对矩条件的选取非常敏感。如何选取最优的矩条件,进而得到更准确的参数估计和更精确的统计推断,是实证研究面临的重要问题。本文从估计量均方误差（MSE）最小的角度,研究了一般矩约束模型两步有效广义矩（GMM）估计的最优矩条件选取方法。首先,利用迭代的方法,推导出两步有效GMM估计的高阶MSE,然后通过Nagar分解,求出了两步有效GMM估计量的近似MSE。接着,基于近似MSE表达式,给出了两步有效GMM估计矩条件选取准则的一般理论,即定义了最优的矩条件,提出了两步有效GMM估计的最优矩条件选取准则,并证明了选取准则的渐近有效性。模拟结果表明,本文提出的矩条件选取方法能够很好地改善两步有效GMM估计量的有限样本表现,降低估计量的有效样本偏差。本研究为实证研究中面临的矩条件选择问题提供了理论依据。     

On Variational Bayes Estimation and Variational Information Criteria for Linear Regression Models

Variational Bayes (VB) estimation is a fast alternative to Markov Chain Monte Carlo for performing approximate Baesian inference. This procedure can be an efficient and effective means of analyzing large datasets. However, VB estimation is often criticised, typically on empirical grounds, for being unable to produce valid statistical inferences. In this article we refute this criticism for one of the simplest models where Bayesian inference is not analytically tractable, that is, the Bayesian linear model (for a particular choice of priors). We prove that under mild regularity conditions, VB based estimators enjoy some desirable frequentist properties such as consistency and can be used to obtain asymptotically valid standard errors. In addition to these results we introduce two VB information criteria: the variational Akaike information criterion and the variational Bayesian information criterion. We show that variational Akaike information criterion is asymptotically equivalent to the frequentist Akaike information criterion and that the variational Bayesian information criterion is first order equivalent to the Bayesian information criterion in linear regression. These results motivate the potential use of the variational information criteria for more complex models. We support our theoretical results with numerical examples.     

论国家表现的国格

国家国格的内涵包括国家与本国公民的国格行为;一国对另一国公民的国格行为;国家与国家之间的国格行为;国家对国际社会的国格行为.认识国家国格具有重大的现实意义和深远的政治意义.     

Inference after separated hypotheses testing: an empirical investigation for linear models

Model selection problems arise while constructing unbiased or asymptotically unbiased estimators of measures known as discrepancies to find the best model. Most of the usual criteria are based on goodness-of-fit and parsimony. They aim to maximize a transformed version of likelihood. For linear regression models with normally distributed error, the situation is less clear when two models are equivalent: are they close to or far from the unknown true model? In this work, based on stochastic simulation and parametric simulation, we study the results of Vuong's test, Cox's test, Akaike's information criterion, Bayesian information criterion, Kullback information criterion and bias corrected Kullback information criterion and the ability of these tests to discriminate between non-nested linear models.     

Adaptive order selection for autoregressive models

Autoregressive model is a popular method for analysing the time dependent data, where selection of order parameter is imperative. Two commonly used selection criteria are the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), which are known to suffer the potential problems regarding overfit and underfit, respectively. To our knowledge, there does not exist a criterion in the literature that can satisfactorily perform under various situations. Therefore, in this paper, we focus on forecasting the future values of an observed time series and propose an adaptive idea to combine the advantages of AIC and BIC but to mitigate their weaknesses based on the concept of generalized degrees of freedom. Instead of applying a fixed criterion to select the order parameter, we propose an approximately unbiased estimator of mean squared prediction errors based on a data perturbation technique for fairly comparing between AIC and BIC. Then use the selected criterion to determine the final order parameter. Some numerical experiments are performed to show the superiority of the proposed method and a real data set of the retail price index of China from 1952 to 2008 is also applied for illustration.     

我国刑事法律援助制度的几个问题及完善——以国际刑事司法准则为视角

法律援助制度是法律程序公正的体现,通过实证考察法律援助的实施状况,从立法方面提出了法律援助范围过窄、可能判处死刑案件的援助阶段缺失以及实践中法律援助的责任主体不适当三个问题,继而研究国际刑事司法准则在这三个问题上设立的国际标准,通过比较提出对我国刑事法律援助制度改善的一些建议。     

对我国“犯罪既遂标准说”的反思——“犯罪对象侵害说”之倡导

"构成要件齐备说"作为犯罪既遂的认定标准存在重大缺陷。王志祥教授、徐光华博士为"构成要件齐备说"所进行的辩护难以奏效。学界对"犯罪目的实现说"、"犯罪结果发生说"还存在着重大误读。新近出现的"类型化标准说"提倡不同的犯罪类型适用不同的既遂标准,其旨在增强犯罪既遂标准理论的实践功效。可是,该学说不仅未跳出"构成要件齐备说"的藩篱,而且又制造了一些新的"麻烦"。当下的"犯罪既遂标准说"存有"立场错误"、"操作性差"等方面的问题,需要我们构建新的"犯罪既遂标准说"———"犯罪对象侵害说"。     

背离与融合:我国逮捕制度的功能与程序

从逮捕功能与程序设置关系的角度进行分析,是研究我国逮捕制度改革的全新视角.我国逮捕制度的预设功能在于,对逮捕进行事前的外部审查,决定是否长期羁押,这种“事前预防”功能应当与审查逮捕程序的“低标准”对应,以实现保护人身自由与保障侦查的平衡.但是,由于我国审查逮捕程序设置的“高标准”,批捕程序无法迅速控制犯罪嫌疑人,导致“事前预防”功能和羁押必要性审查机制的虚置.改革应当着眼于制度功能与程序配置的融合,降低审查逮捕的程序标准,以实现逮捕的功能.     

城市文化——罪恶的渊薮——苏童小说《米》的再解读

苏童摆脱了对创作短篇小说的一度迷恋,写作了长篇"新写实小说"《米》。《米》已经被人们做了诸多的归纳与阐释,但最根本的还是要借一个逃亡与回归的故事母题,去写一个在城市文化中人性被扭曲的、孤独无依的人的具有悲剧意义的一生。于是逃亡与回归、人性的扭曲、生命的孤独便构成了苏童小说《米》叙述的基本元素。本文就试图通过这些跳荡在作品中的元素,来透视这些元素背后的城市的淫靡文化是如何摧毁了一个乡村青年的生命的。     

An efficient and fast algorithm for estimating the parameters of two-dimensional sinusoidal signals

In this paper we propose a computationally efficient algorithm to estimate the parameters of a 2-D sinusoidal model in the presence of stationary noise. The estimators obtained by the proposed algorithm are consistent and asymptotically equivalent to the least squares estimators. Monte Carlo simulations are performed for different sample sizes and it is observed that the performances of the proposed method are quite satisfactory and they are equivalent to the least squares estimators. The main advantage of the proposed method is that the estimators can be obtained using only finite number of iterations. In fact it is shown that starting from the average of periodogram estimators, the proposed algorithm converges in three steps only. One synthesized texture data and one original texture data have been analyzed using the proposed algorithm for illustrative purpose.