Estimation of the Memory Parameters of the Fractionally Integrated Separable Spatial Autoregressive (FISSAR(1, 1)) Model: A Simulation Study

In this article, we implement the Regression Method for estimating (d ₁, d ₂) of the FISSAR(1, 1) model. It is also possible to estimate d ₁ and d ₂ by Whittle's method. We also compute the estimated bias, standard error, and root mean square error by a simulation study. A comparison was made between the Regression Method of estimating d ₁ and d ₂ to that of the Whittle's method. It was found in this simulation study that the Regression Method of estimation was better when compare with the Whittle's estimator, in the sense that it had smaller root mean square errors (RMSE) values.     

Jackknifing the Ridge Regression Estimator: A Revisit

Singh et al. (1986 Singh, B., Chaubey, Y.P., Dwivedi, T.D. (1986). An almost unbiased ridge estimator. Sankhya B48: 34236. [Google Scholar]) proposed an almost unbiased ridge estimator using Jackknife method that required transformation of the regression parameters. This article shows that the same method can be used to derive the Jackknifed ridge estimator of the original (untransformed) parameter without transformation. This method also leads in deriving easily the second-order Jackknifed ridge that may reduce the bias further. We further investigate the performance of these estimators along with a recent method by Batah et al. (2008 Batah, F. S.M., Ramanathan, T.V., Gore, S.D. (2008). The efficiency of modified Jack-knife and ridge type regression estimators: a comparison. Surv. Math. Applic. 3:111122. [Google Scholar]) called modified Jackknifed ridge theoretically as well as numerically.     

A First-Order Spatial Integer-Valued Autoregressive SINAR(1, 1) Model

Binomial thinning operator has a major role in modeling one-dimensional integer-valued autoregressive time series models. The purpose of this article is to extend the use of such operator to define a new stationary first-order spatial non negative, integer-valued autoregressive SINAR(1, 1) model. We study some properties of this model like the mean, variance and autocorrelation function. Yule-Walker estimator of the model parameters is also obtained. Some numerical results of the model are presented and, moreover, this model is applied to a real data set.     

论知识经济时代的工商管理本科教育创新

知识经济时代的到来,对人们社会生活方式和思维方式产生重大的影响,并对传统的工商管理本科教育理念、教学组织、教学方式、教学内容、教学过程、教学手段产生巨大的冲击。面对知识经济的这种要求和21世纪的时代特点,我国传统的工商管理本科教育模式是难以适应的,必须更新观念,研究未来社会对工商管理人才知识、能力和素质结构的要求,改变人才培养模式,建立通才和专才并举,知识、能力与素质结合的工商管理现代人才培养模式,进行工商管理本科教育的创新。     

Autocovariance Function of the Fractionally Integrated Separable Spatial ARMA (FISSARMA) Models

Spatial modeling is important in many fields and there are various kinds of spatial models. One of such models is known as the fractionally integrated separable spatial ARMA (FISSARMA) model. In the area of time series analysis, Sowell (1992 Sowell, F. (1992). Maximum likelihood estimation of stationary univariate fractionally integrated time series models. J. Econ. 53:165–188.[Crossref], [Web of Science ®] , [Google Scholar]) has established the autocovariance function of the long-memory models using hypergeometric function. In this paper we will extend Sowell’s work for FISSARMA models.     

Vector and recurrent singular spectrum analysis: which is better at forecasting?

Singular spectrum analysis (SSA) is an increasingly popular and widely adopted filtering and forecasting technique which is currently exploited in a variety of fields. Given its increasing application and superior performance in comparison to other methods, it is pertinent to study and distinguish between the two forecasting variations of SSA. These are referred to as Vector SSA (SSA-V) and Recurrent SSA (SSA-R). The general notion is that SSA-V is more robust and provides better forecasts than SSA-R. This is especially true when faced with time series which are non-stationary and asymmetric, or affected by unit root problems, outliers or structural breaks. However, currently there exists no empirical evidence for proving the above notions or suggesting that SSA-V is better than SSA-R. In this paper, we evaluate out-of-sample forecasting capabilities of the optimised SSA-V and SSA-R forecasting algorithms via a simulation study and an application to 100 real data sets with varying structures, to provide a statistically reliable answer to the question of which SSA algorithm is best for forecasting at both short and long run horizons based on several important criteria.     

企业的社会责任与人力资源管理创新

SA8000进入我国已有4年多时间,但我国企业并没有多大的认证兴趣,甚至很多企业根本就不知道这一标准的存在。我国企业应该认清SA8000是企业承担社会责任的初级标准选择,并且应该积极主动地去承担;企业要制定相应的人力资源管理战略,支撑企业承担社会责任的战略目标;企业要依据SA8000标准,对人力资源管理职能流程链进行改进和提升,使企业承担社会责任落实到实处。     

中小民营企业融资渠道现状与特征探析——以广东实证分析为例

根据中国人民银行广州分行课题组的调查结果,从现有融资渠道及其结构状况与特点、不同资产规模企业的融资渠道结构差异比较、不同地区企业的融资渠道结构差异比较三个角度对广东中小民营企业融资渠道的状况与特征进行实证分析。结果表明广东省中小民营企业融资难问题突出表现在:短期资金融资难度降低,但长期权益性资本严重短缺;中小民营企业中做大的企业融资困难得到缓解,但中小企业融资仍十分困难;发达地区资金相对充足,欠发达地区中小民营企业融资更为困难。     

Some Properties of the Normalized Periodogram of a Fractionally Integrated Separable Spatial ARMA (FISSARMA) Model

In this article, we study the properties of the normalized periodogram of the Fractionally Integrated Separable Spatial ARMA (FISSARMA) models. In particular, we establish the asymptotic mean of the normalised periodogram and the asymptotic second-order moments of the normalised Fourier coefficients. We also establish the asymptotic distribution of the normalised periodogram. Some numerical results are also provided.