LS estimation of periodic autoregressive models with non-Gaussian errors: a simulation study

In this article, the least squares (LS) estimates of the parameters of periodic autoregressive (PAR) models are investigated for various distributions of error terms via Monte-Carlo simulation. Beside the Gaussian distribution, this study covers the exponential, gamma, student-t, and Cauchy distributions. The estimates are compared for various distributions via bias and MSE criterion. The effect of other factors are also examined as the non-constancy of model orders, the non-constancy of the variances of seasonal white noise, the period length, and the length of the time series. The simulation results indicate that this method is in general robust for the estimation of AR parameters with respect to the distribution of error terms and other factors. However, the estimates of those parameters were, in some cases, noticeably poor for Cauchy distribution. It is also noticed that the variances of estimates of white noise variances are highly affected by the degree of skewness of the distribution of error terms.     

On the accuracy and cost of numerical integration in several variables ∗

Identical numerical integration experiments are performed on a CYBER 205 and an IBM 3081 in order to gauge the relative performance of several methods of integration. The methods employed are the general methods of Gauss-Legendre, iterated Gauss-Legendre, Newton-Cotes, Romberg and Monte Carlo as well as three methods, due to Owen, Dutt, and Clark respectively, for integrating the normal density. The bi- and trivariate normal densities and four other functions are integrated; the latter four have integrals expressible in closed form and some of them can be parameterized to exhibit singularities or highly periodic behavior. The various Gauss-Legendre methods tend to be most accurate (when applied to the normal density they are even more accurate than the special purpose methods designed for the normal) and while they are not the fastest, they are at least competitive. In scalar mode the CYBER is about 2-6 times faster than the IBM 3081 and the speed advantage of vectorised to scalar mode ranges from 6 to 15. Large scale econometric problems of the probit type should now be routinely soluble.     

固定效应部分线性变系数面板模型的快速有效估计

本文将最小二乘支持向量机(LSSVM) 和二次推断函数法(QIF) 相结合，为个体内具有相关结构的固定效应部分线性变系数面板模型提供了一种新的快速估计方法；在一定的正则条件下，论证了参数估计量的渐近正态性和非参数估计量的收敛速度；采用Monte Carlo模拟考察了估计方法在有限样本下的表现并将估计技术应用于现实数据分析。该方法不仅保证了估计的有效性和统计推断力，而且程序运行速度得到较大幅度提升。     

胡尖山地区长 6、长 4+5 储层有利区预测

摘要：以胡尖山油田长 6、长 4+5 油藏为例，综合应用地质资料、岩芯分析资料、测井资料以及试油资料，开展了长 6和长 4+5 油藏小层划分和对比、单井相分析，建立测井相识别模式，编制小层沉积相平面展布图，进而分析沉积相演化规律；探讨储层砂体展布以及物性分布规律，从而深入认识研究区的储层特征。在此基础上对储层进行流动单元的划分，然后结合试油资料对胡尖山油田长 6、长 4+5 油藏的储层进行有利区域的预测，为油田待开发区域的合理建产开发提供了理论指导     

在线评论回报的动态声誉机制研究

当前学界研究主要集中于网络评论的声誉机制或虚假评论的识别策略,好评返现可以看作是声誉的交易(消费者给予好评,商家报以返现),而对此类带有一定贿赂性质的、诱导性的评论的声誉机制研究不多。本文以某网络购物平台2016和2017年两次追踪调查数据为样本,实证研究了好评返现是如何作用于最终声誉评价的。实证结果发现,好评返现对店铺声誉累积的实际影响并不是完全正向的,而是以店铺初始声誉等级为阈值,对于信誉等级较高的店铺(品牌商家)不敏感,对于信誉等级较低的店铺(新进卖家)比较敏感;商家是否参与好评返现并非内生于店铺的初始信誉评价,而更多的是提升信誉评分或对冲部分负面评论;消费者是否参与好评返现与其行为意愿和态度不存在显著关联,匿名环境往往使消费者更易做出内心并不认同却能获益的行为。     

教养方式对不同性别学前儿童羞怯的影响：基于模糊集定性比较分析（fsQCA）

为探讨父母教养方式对不同性别学前儿童羞怯的组合作用路径,研究采用模糊集定性比较分析（fsQCA）,考察了532名学前儿童的性别和教养方式四个维度（说理、随和、命令、言语攻击）共5个条件变量影响儿童羞怯的条件组态。结果发现：1）单个父母教养方式不是影响学前儿童羞怯的必要条件,教养方式以组态的方式影响学前儿童羞怯;2）影响“儿童羞怯”的三条组合路径分别是“女孩*非说理”、“女孩*命令*言语攻击”、“非说理*命令”;影响“非儿童羞怯”的三条组合路径分别是“男孩*非言语攻击”、“说理*非说理*随和”。     

后发企业如何构建创新网络—基于知识架构的视角

构建创新网络是后发企业快速进行技术追赶的重要路径。那么,针对不同的企业,构筑什么样的创新网络是有效的? 为了深化以往的创新网络研究,从知识架构的视角出发,聚焦于网络层次的合作伙伴配置,构建了以产业特征为情境条件,知识基宽度/深度为焦点企业特征,知识异质性/质量为创新网络节点组成特征,高创新追赶绩效为结果变量的逻辑模型。结论显示,有三种相应的架构类型能够获取高创新追赶绩效。此外,在不同的产业特征情境下,不同的企业分别可以采用相应的架构类型取得更好的绩效。以此,为后发企业构建有效创新网络提供了理论和实践的借鉴。     

On distribution-weighted partial least squares with diverging number of highly correlated predictors

Summary.  Because highly correlated data arise from many scientific fields, we investigate parameter estimation in a semiparametric regression model with diverging number of predictors that are highly correlated. For this, we first develop a distribution-weighted least squares estimator that can recover directions in the central subspace, then use the distribution-weighted least squares estimator as a seed vector and project it onto a Krylov space by partial least squares to avoid computing the inverse of the covariance of predictors. Thus, distrbution-weighted partial least squares can handle the cases with high dimensional and highly correlated predictors. Furthermore, we also suggest an iterative algorithm for obtaining a better initial value before implementing partial least squares. For theoretical investigation, we obtain strong consistency and asymptotic normality when the dimension p of predictors is of convergence rate O { n ^1/2/ log ( n )} and o ( n ^1/3) respectively where n is the sample size. When there are no other constraints on the covariance of predictors, the rates n ^1/2 and n ^1/3 are optimal. We also propose a Bayesian information criterion type of criterion to estimate the dimension of the Krylov space in the partial least squares procedure. Illustrative examples with a real data set and comprehensive simulations demonstrate that the method is robust to non-ellipticity and works well even in 'small n –large p ' problems.     

Comparison of methods for analyzing binary repeated measures data: A simulation-based study (comparison of methods for binary repeated measures)

In this study, some methods suggested for binary repeated measures, namely, Weighted Least Squares (WLS), Generalized Estimating Equations (GEE), and Generalized Linear Mixed Models (GLMM) are compared with respect to power, type 1 error, and properties of estimates. The results indicate that with adequate sample size, no missing data, the only covariate being time effect, and a relatively limited number of time points, the WLS method performs well. The GEE approach performs well only for large sample sizes. The GLMM method is satisfactory with respect to type I error, but its estimates have poorer properties than the other methods.