从普通话水平测试看高师普通话教学

高师院校的普通话教学与快速发展的普通话水平测试工作如何相互适应是摆在教学工作者面前的重要课题.教师应该有重点、有针对性地进行教学,并且多层次、多渠道地采取有效措施提高学生普通话水平,使之顺利通过普通话水平测试.另外,作为一名合格的普通话水平测试员,不断提高自身素质,加强德与才的修养是最为必要的.     

基于ECM模型的城镇居民收入与消费问题的实证研究——以杭州为例

本文根据协整理论与误差修正模型,利用1978-2007年度数据以杭州市为例对城镇居民收入与消费的协整关系进行了实证分析。结果表明:杭州市城镇居民家庭的人均消费与收入之间存在单向长期稳定的因果关系,即人均可支配收入变动是影响人均消费变动的原因,而人均消费变动不是影响人均可支配收入变动的原因。因此,从长期来看,要刺激城镇居民消费来拉动经济增长,必须增加城镇居民的可支配收入。     

Mean-Minimum Exact Confidence Intervals

This article introduces mean-minimum (MM) exact confidence intervals for a binomial probability. These intervals guarantee that both the mean and the minimum frequentist coverage never drop below specified values. For example, an MM 95[93]% interval has mean coverage at least 95% and minimum coverage at least 93%. In the conventional sense, such an interval can be viewed as an exact 93% interval that has mean coverage at least 95% or it can be viewed as an approximate 95% interval that has minimum coverage at least 93%. Graphical and numerical summaries of coverage and expected length suggest that the Blaker-based MM exact interval is an attractive alternative to, even an improvement over, commonly recommended approximate and exact intervals, including the Agresti–Coull approximate interval, the Clopper–Pearson (CP) exact interval, and the more recently recommended CP-, Blaker-, and Sterne-based mean-coverage-adjusted approximate intervals.     

Rates of convergence of autocorrelation estimates for periodically correlated autoregressive Hilbertian processes

Autoregressive Hilbertian (ARH) processes are of great importance in the analysis of functional time series data and estimation of the autocorrelation operators attracts the attention of various researchers. In this paper, we study estimators of the autocorrelation operators of periodically correlated autoregressive Hilbertian processes of order one (PCARH(1)), which is an extension of ARH(1) processes. The estimation method is based on the spectral decomposition of the covariance operator and considers two main cases: known and unknown eigenvectors. We show the consistency in the mean integrated quadratic sense of the estimators of the autocorrelation operators and present upper bounds for the corresponding rates.     

The Network Autocorrelation Model using Two-mode Data: Affiliation Exposure and Potential Bias in the Autocorrelation Parameter

The network autocorrelation model has been a workhorse for modeling network influences on individual behavior. The standard network approaches to mapping social influence using network measures, however, are limited to specifying an influence weight matrix (W) based on a single mode network. Additionally, it has been demonstrated that the estimate of the autocorrelation parameter ρ of the network effect tends to be negatively biased as the density in W matrix increases. The current study introduces a two-mode version of the network autocorrelation model. We then conduct simulations to examine conditions under which bias might exist. We show that the estimate for the affiliation autocorrelation parameter (ρ) tends to be negatively biased as density increases, as in the one-mode case. Inclusion of the diagonal of W, the count of the number of events participated in, as one of the variables in the regression model helps to attenuate such bias, however. We discuss the implications of these results.     

Relative efficiency of first difference estimator in panel data regression with serially correlated error components

We consider the pooled cross-sectional and time series regression model when the disturbances follow a serially correlated one-way error components. In this context we discovered that the first difference estimator for the regression coefficients is equivalent to the generalized least squares estimator irrespective of the particular form of the regressor matrix when the disturbances are generated by a first order autoregressive process where the autocorrelation is close to unity.     

Relative efficiency of OLSE and COTE for seasonal autoregressive disturbances

The efficiency of OLSE relative to GLSE and COTE is studied in the case in which regressors are splines used to explain seasonal influences. It is thereby shown that efficiency measured as the ratio of total or generalized variances is independent of the actual design of splines. Furthermore, for positive autocorrelation, COTE is always worse than OLSE.     

A Modified Knox Test of Space-Time Clustering

Tests of space-time clustering such as the Knox test are used by epidemiologists in the preliminary analysis of datasets where an infectious aetiology is suspected. The Knox test statistic is the number of cases close in both space and time to another case. The test statistic proposed here is the excess number of such cases over that expected under H₀ of no infection. It is argued that this modified test is more powerful than the Knox test, because the test statistic is not heavily tied as is the Knox test statistic. The use of the test is illustrated with examples.     

协整理论和ECM模型在我国经济增长因素分析中的应用

文章运用协整理论,建立了排除伪回归的长期均衡方程以及ECM模型,对影响经济增长的资本因素、劳动因素、制度因素和产业结构因素进行分析,从定量角度探寻长期和短期影响我国经济增长的主要因素。     

高新技术产业发展与科技投入的关系分析

探索R&D科技投入与高新技术产业产出的关系,对于增加科技投入以促进该产业的发展具有重要作用。在VAR模型估计的基础上,运用协整理论、因果检验和误差修正模型,分析了1995-2010年科技投入与高技术产业产出之间的动态关系。结论表明:科技投入是高技术产业发展的格兰杰原因,在长期内能明显促进该产业的增长,但短期效果有限。因此,要求我们应当增加对高新技术产业的长期科技投入,为其长远发展奠定基础。