GMM Estimation of Count-Panel-Data Models With Fixed Effects and Predetermined Instruments

The “traditional” approach to the estimation of count-panel-data models with fixed effects is the conditional maximum likelihood estimator. The pseudo maximum likelihood principle can be used in these models to obtain orthogonality conditions that generate a robust estimator. This estimator is inconsistent, however, when the instruments are not strictly exogenous. This article proposes a generalized method of moments estimator for count-panel-data models with fixed effects, based on a transformation of the conditional mean specification, that is consistent even when the explanatory variables are predetermined. Two applications are discussed, the relationship between patents and research and development expenditures and the explanation of technology transfer.     

Restricted estimation and testing of hypothesis in linear measurement errors models

ABSTRACT

In this article, the linear models with measurement error both in the response and in the covariates are considered. Following Shalabh et al. (2007 Shalabh, Garg, G., Misra, N. (2007). Restricted regression estimation in measurement error models. Comput. Stat. Data Anal. 52:1149–1166.[Crossref], [Web of Science ®] , [Google Scholar], 2009 Shalabh, Garg, G., Misra, N. (2009). Use of prior information in the consistent estimation of regression coefficients in measurement error models. J. Multivariate Anal. 100:1498–1520.[Crossref], [Web of Science ®] , [Google Scholar]), we propose several restricted estimators for the regression coefficients. The consistency and asymptotic normality of the restricted estimators are established. Furthermore, we also discuss the superiority of the restricted estimators to unrestricted estimators under Pitman closeness criterion. We also develop several variance estimators and establish their asymptotic distributions. Wald-type statistics are constructed for testing the linear restrictions. Finally, Monte Carlo simulations are conducted to illustrate the finite-sample properties of the proposed estimators.     

Identification of target clusters by using the restricted normal mixture model

This paper addresses the problem of identifying groups that satisfy the specific conditions for the means of feature variables. In this study, we refer to the identified groups as “target clusters” (TCs). To identify TCs, we propose a method based on the normal mixture model (NMM) restricted by a linear combination of means. We provide an expectation–maximization (EM) algorithm to fit the restricted NMM by using the maximum-likelihood method. The convergence property of the EM algorithm and a reasonable set of initial estimates are presented. We demonstrate the method's usefulness and validity through a simulation study and two well-known data sets. The proposed method provides several types of useful clusters, which would be difficult to achieve with conventional clustering or exploratory data analysis methods based on the ordinary NMM. A simple comparison with another target clustering approach shows that the proposed method is promising in the identification.     

机械故障特征提取分析及其维修决策

对重型车辆机械故障特点及共同特征进行了剖析和概括,介绍了目前常用于故障信号检测的方法,对故障识别方法进行了探讨。为了预估重型车辆的剩余寿命和故障发生的时间、部位及严重程度,利用灰色预测方法对重型车辆运行状况进行预测,为维修决策提供依据。     

有限公司股东退股制度立法评析——兼评公司法第75条之规定

有限公司股东退股属于公司取得自己股份的一种情形,其基本价值在于保护中小股东的合法权益。我国公司法虽然对有限公司股东退股请求权规定了适用的条件,但是也存在欠缺,如对于有限公司取得本公司股份的法律地位和处分等都没有作出规定,因而立法上需要明确。     

对跨国公司限制竞争行为和垄断的防范对策

本文在分析了跨国公司在我国的限制竞争行为及垄断的表现和产生原因的基础上,有针对性地提出了对跨国公司限制竞争和垄断行为进行有效防范和监控的对策。     

Ridge regression methodology in partial linear models with correlated errors

In this article, a generalized restricted difference-based ridge estimator is defined for the vector parameter in a partial linear model when the errors are dependent. It is suspected that some additional linear constraints may hold on to the whole parameter space. The estimator is a generalization of the well-known restricted least-squares estimator and is confined to the (affine) subspace which is generated by the restrictions. The risk functions of the proposed estimators are derived under balanced loss function. Finally, the performance of the new estimators is evaluated by a simulated data set.     

A comparative simulation study of data-driven methods for estimating density level sets

Density level sets are mainly estimated using one of three methodologies: plug-in, excess mass, or a hybrid approach. The plug-in methods are based on replacing the unknown density by some nonparametric estimator, usually the kernel one. Thus, the bandwidth selection is a fundamental problem from an applied perspective. Recently, specific selectors for level sets have been proposed. However, if some a priori information about the geometry of the level set is available, then excess mass algorithms can be useful. In this case, the problem of bandwidth selection can be avoided. The third methodology is a hybrid of the others. It assumes a mild geometric restriction on the level set and it requires a pilot nonparametric estimator of the density. One interesting open question concerns the performance of these methods. In this work, existing methods are reviewed, and two new hybrid algorithms are proposed. Their practical behaviour is compared through extensive simulation study.     

Bayesian regression under combinations of constraints

A Bayesian method for regression under several types of constraints is proposed. The constraints can be range-restricted and include shape restrictions, constraints on the value of the regression function, smoothness conditions and combinations of these types of constraints. The support of the prior distribution is included in the set of piecewise linear functions. It is shown that the proposed prior can be arbitrarily close to the distribution induced by the addition of a polynomial plus an (m−1)-fold integrated Brownian motion. Hence, despite its piecewise linearity, the regression function behaves (approximately) like an m−1 times continuously differentiable random function. Furthermore, thanks to the piecewise linear property, many combinations of constraints can easily be considered. The regression function is estimated by the posterior mode computed by a simulated annealing algorithm. The constraints on the shape and the values of the regression function are taken into account thanks to the proposal distribution, while the smoothness condition is handled by the acceptation step. Simulations from the posterior distribution are obtained by a Gibbs sampling algorithm.     

行为者中心限制与行为者中心特权——谢弗勒“混合理论”述评

谢弗勒是美国当代重要的伦理学家。为回应威廉斯对后果主义破坏人的完整性异议,他提出了行为者中心限制和行为者特权的“混合理论”。这一理论前半部分强调行为者受到道义论的限制,从而不可能为了后果最大化好而做后果主义所要求的事;后半部分则认为人们可以追求行动最大化好的后果,但并不应把后果主义的原则应用到一切行为之中,同时认为,行为者有自己的特权,即可以为了自己的利益追求放大M倍来为自己辩护。谢弗勒的“混合理论”两个方面都受到批评,其实是一种对后果主义的有益改进。