互联网+“国际双师教学”:促进高等教育国际化发展的新途径

随着中国建设世界一流大学和一流学科的“双一流”水平的不断提高,国际化已经成为中国高等教育的发展战略之一。实现高等教育的国际化,必然需要具有先进的国际教育理念、共享优质的国际教育资源、引进先进的国际课程和高水平的外籍师资。在对中国国内互联网+“双师教学”进行追溯和分析的基础上,提出了互联网+“国际双师教学”这一新的教育模式,旨在期待其可以成为有效促进中国高等教育国际化发展的新途径。     

Social capital online: Collective use of the Internet and reciprocity as lubricants of democracy

This article puts the democratic potential of using the Internet into perspective through an analysis of how collective uses of the Internet promote social capital. Research results reveal that social capital online (i.e. trust and reciprocity) is enhanced by involvement in collective use of the Internet such as participation in online communities and use of the Internet among informal groups in everyday life. This process could counter negative aspects of Internet use. Further, accumulated online social capital can be a powerful predictor of online political participation, i.e. online reciprocity has a positive effect on intention to participate in online civic discussion. Finally, the authors' analyses indicate the possibility of a spillover of online social capital into offline arenas. It is concluded that collective use of the Internet can be a lubricant for democracy.     

On the level probabilities for useful partially ordered alternatives in the analysis of variance

In the analysis of variance, we often encounter situations in which we want to test the null hypothesis of homogeneity of the normal means against various partially ordered alternative hypotheses. We study likelihood ratio tests for three useful types of alternatives: d-star, bipartite and broom tree. Especially, we give computational formulas for the level probabilities of the alternative types. The results permit us to obtain critical values for practical use.     

Bootstrap estimation and model selection for multivariate normal mixtures using parallel computing with graphics processing units

In applications of multivariate finite mixture models, estimating the number of unknown components is often difficult. We propose a bootstrap information criterion, whereby we calculate the expected log-likelihood at maximum a posteriori estimates for model selection. Accurate estimation using the bootstrap requires a large number of bootstrap replicates. We accelerate this computation by employing parallel processing with graphics processing units (GPUs) on the Compute Unified Device Architecture (CUDA) platform. We conducted a runtime comparison of CUDA algorithms between implementation on the GPU and that on a CPU. The results showed significant performance gains in the proposed CUDA algorithms over multithread CPUs.     

On least-squares bias in the AR(p) models: Bias correction using the bootstrap methods

In the case where the lagged dependent variables are included in the regression model, it is known that the ordinary least squares estimates (OLSE) are biased in small sample and that the bias increases as the number of the irrelevant variables increases. In this paper, based on the bootstrap methods, an attempt is made to obtain the unbiased estimates in autoregressive and non-Gaussian cases. We propose the residual-based bootstrap method in this paper. Some simulation studies are performed to examine whether the proposed estimation procedure works well or not. We obtain the results that it is possible to recover the true parameter values and that the proposed procedure gives us the less biased estimators than OLSE. This paper is a substantial revision of Tanizaki (2000). The normality assumption is adopted in Tanizaki (2000), but it is not required in this paper. The authors are grateful to an anonymous referee for valuable suggestions and comments. This research was partially supported by Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research (C)(2) #14530033, 2002–2005, for H. Tanizaki and Grants-in-Aid for the 21st Century COE Program.     

Testing for the Null Hypothesis of Cointegration with a Structural Break

In this paper we propose residual-based tests for the null hypothesis of cointegration with a structural break against the alternative of no cointegration. The Lagrange Multiplier (LM) test is proposed and its limiting distribution is obtained for the case in which the timing of a structural break is known. Then the test statistic is extended to deal with a structural break of unknown timing. The test statistic, a plug-in version of the test statistic for known timing, replaces the true break point by the estimated one. We show the limiting properties of the test statistic under the null as well as the alternative. Critical values are calculated for the tests by simulation methods. Finite-sample simulations show that the empirical size of the test is close to the nominal one unless the regression error is very persistent and that the test rejects the null when no cointegrating relationship with a structural break is present. We provide empirical examples based on the present-value model, the term structure model, and the money-output relationship model.     

THE IMPACT OF TRADE LIBERALIZATION ON INDUSTRIAL PRODUCTIVITY

An empirical finding by Trefler (2004, “The Long and Short of the Canada–U.S. Free Trade Agreement”, American Economic Review, 94(4), 870–895) and others that industrial productivity increases more strongly in liberalized industries than in nonliberalized industries has been widely accepted as evidence for the Melitz ( 2003 , “The Impact of Trade on Intra‐Industry Reallocations and Aggregate Industry Productivity”, Econometrica, 71, 1695–1725) model. We show that under fairly standard assumptions a multi‐industry version of the Melitz model does not predict this relationship. Instead, it predicts the opposite relationship that industrial productivity increases more strongly in nonliberalized industries than in liberalized industries.     

Maximum likelihood estimators in finite mixture models with censored data

The consistency of estimators in finite mixture models has been discussed under the topology of the quotient space obtained by collapsing the true parameter set into a single point. In this paper, we extend the results of Cheng and Liu (2001) to give conditions under which the maximum likelihood estimator (MLE) is strongly consistent in such a sense in finite mixture models with censored data. We also show that the fitted model tends to the true model under a weak condition as the sample size tends to infinity.