Model Selection for Vector Autoregressive Processes via Adaptive Lasso

Determination of the best subset is an important step in vector autoregressive (VAR) modeling. Traditional methods either conduct subset selection and parameter estimation separately or compute expensively. In this article, we propose a VAR model selection procedure using adaptive Lasso, for it is computational efficient and can select subset and estimate parameters simultaneously. By proper choice of tuning parameters, we can choose the correct subset and obtain the asymptotic normality of the non zero parameters. Simulation studies and real data analysis show that adaptive Lasso performs better than existing methods in VAR model fitting and prediction.     

Empirical Likelihood-based Inference for Stationary-ergodicity of the Generalized Random Coefficient Autoregressive Model

In this article, we consider the application of the empirical likelihood method to the generalized random coefficient autoregressive (GRCA) model. When the order of the model is 1, we derive an empirical likelihood ratio test statistic to test the stationary-ergodicity. Some simulation studies are also conducted to investigate the finite sample performances of the proposed test.     

Estimation in second order branching processes with application to swine flu data

Abstract

This paper discusses inferential issues related to estimation of offspring mean and variance in a second order branching process, when both the offspring distributions are assumed to have identical mean and variance. Estimating equation approach is used to find the estimator of the offspring mean and the fact that a second order branching process model can be modeled as an autoregressive process is utilized to obtain the estimator of the offspring variance. Both the estimators are shown to be consistent and asymptotically normal. The second order branching process model is applied to H1N1 data for Pune, India, and Mexico and is found to be a suitable model. The estimates obtained from this model are used to compute the proportion of vaccination required for elimination of the disease.     

A GRAPHICAL TECHNIQUE FOR DETECTING INFLUENTIAL CASES IN REGRESSION ANALYSIS

This paper presents a graphical technique for detecting influential cases in regression analysis. The idea is to decompose a diagnostic problem involving higher order dimensional regression problems, into a series of two-dimensional diagnostic sub-problems, such that the diagnoses of influential cases is undertaken by visually inspecting two-dimensional diagnostic plots of these sub-problems. An algorithm for the graphical procedure is proposed to reduce the computational effort. Practical examples are used to illustrate this graphical technique.     

Artificial neural networks for some macroeconomic series: A first report

There is a tendency for the true variability of feasible GLS estimators to be understated by asymptotic standard errors. For estimation of SUR models, this tendency becomes more severe in large equation systems when estimation of the error covariance matrix, C, becomes problematic. We explore a number of potential solutions involving the use of improved estimators for the disturbance covariance matrix and bootstrapping. In particular, Ullah and Racine (1992) have recently introduced a new class of estimators for SUR models that use nonparametric kernel density estimation techniques. The proposed estimators have the same structure as the feasible GLS estimator of Zellner (1962) differing only in the choice of estimator for C. Ullah and Racine (1992) prove that their nonparametric density estimator of C can be expressed as Zellner's original estimator plus a positive definite matrix that depends on the smoothing parameter chosen for the density estimation. It is this structure of the estimator that most interests us as it has the potential to be especially useful in large equation systems.

Atkinson and Wilson (1992) investigated the bias in the conventional and bootstrap estimators of coefficient standard errors in SUR models. They demonstrated that under certain conditions the former were superior, but they caution that neither estimator uniformly dominated and hence bootstrapping provides little improvement in the estimation of standard errors for the regression coefficients. Rilstone and Veal1 (1996) argue that an important qualification needs to be made to this somewhat negative conclusion. They demonstrated that bootstrapping can result in improvements in inferences if the procedures are applied to the t-ratios rather than to the standard errors. These issues are explored for the case of large equation systems and when bootstrapping is combined with improved covariance estimation.     

Ridge Regression for Estimation of Transition Probabilities from Aggregate Data

When data from several independent Markov chains are aggregated over each time point, least square estimation of transition probabilities faces the problem of multi-collinearity. We propose here an estimation procedure which involves use of ridge regression for the ordinary least square estimators. Performance of this estimator is then compared with that of the ordinary least squares.     

新形势下电视新闻市场资源配置研究

电视新闻曾经带动了新闻传播的革命。但在今天的数字化传播兴旺发达的新形势下,电视面对网络媒体的飞速发展及其威胁,应该及时把握问题,发现对策。本文分析了中国电视新闻近期的现状、存在的问题,并在此基础上提出了解决方法;并指出只有最合理地配置电视新闻的市场资源,才能更好地发挥“第一传媒”的社会作用。     

邯郸市医疗资源共享的模式研究

通过对邯郸市医疗资源配置调查,分析医疗资源配置中存在的主要问题,提出邯郸市医疗设备共享,检验报告通用,医院社区相互转诊,县以上医疗机构托管乡镇卫生院,远程可视医疗等医疗资源共享模式,致力实现邯郸市城乡医疗机构优势互补、资源共享,解决人们看病难的问题。     

幼儿高科学问题解决能力的家庭科学资本要素组态构型分析

提升幼儿科学问题解决能力对于幼儿科学学习与发展具有重要价值。为充分发挥家庭提升幼儿科学问题解决能力的功效，本研究基于科学资本概念尝试构建家庭科学资本框架，选取J省297对中班和大班幼儿及其父母为研究对象，采用模糊集定性比较分析方法探析家庭科学资本各变量对幼儿科学解决问题能力的内部作用机理。结果发现：家庭科学资本触发高科学问题解决能力的组合路径有7条，可分为4种条件组合构型：科学活动主导驱动型；科学期望-活动协同型；科学期望主导驱动型；科学职业-期望-效能感协同型。研究建议：家长应对幼儿科学能力抱有积极期望，努力为幼儿提供丰富多样的科学活动和资源，发挥自身科学职业优势支持幼儿科学学习；幼儿园应了解幼儿家庭占有科学资本的情况进而提供针对性的指导，并与家庭、科学场馆协同促进幼儿科学学习与发展。     

邯郸市医疗资源共享的模式研究

通过对邯郸市医疗资源配置调查,分析医疗资源配置中存在的主要问题,提出邯郸市医疗设备共享,检验报告通用,医院社区相互转诊,县以上医疗机构托管乡镇卫生院,远程可视医疗等医疗资源共享模式,致力实现邯郸市城乡医疗机构优势互补、资源共享,解决人们看病难的问题。