Managing competence acquisition and financial performance: An empirical study of how small firms use competence acquisition strategies

Past research has neglected how small firms manage competence acquisition. Based on transaction cost literature, this article identifies competence acquisition management strategies and their implications for performance. We explore this issue using survey data from 842 small, knowledge-intensive firms. The results outline four aspects of competence acquisition management: (1) competence absorbers, (2) social acquirers, (3) market acquirers, and (4) nonacquirers. Furthermore, we hypothesized and found that market acquirers score higher in terms of financial performance than firms following the other strategies. The market acquirer strategy proved particularly effective under conditions of high dynamism.     

Estimating Long and Short Run Effects in Static Panel Models

This paper assesses the biases of four different estimators with respect to the short run and the long run parameters if a static panel model is used, although the data generating process is a dynamic error components model. We analytically derive the associated biases and provide a discussion of the determinants thereof. Our analytical and numerical results as well as Monte Carlo simulations illustrate that the asymptotic bias of both the within and the between parameter with respect to the short run and long run impact can be substantial, depending on the memory of the data generating process, the length of the time series and the importance of the cross-sectional variation in the explanatory variables.     

经济发达地区小城镇职业教育模式研究

文章通过对经济发达地区小城镇职业教育发展状况的总结,提出其职业教育模式,并对模式的应用与发展进行了分析,以期对我国小城镇职业教育的发展起到一定参考作用。     

小城市婚姻家庭十年发展变化对比研究——对青岛胶州县改市后婚姻家庭的追踪调查

1995年青岛胶州同全国相当数量的县一样,因城市化的需要进行了县改市,成为就地就近以转化吸收农业转业人口为主的小城市。当时关于"县改市对婚姻家庭影响的调查"就是在该地的辛庄小区进行的,这次原课题组又对辛庄小区进行了全面的追踪调查。经过十年的城市化过程,该地在婚姻家庭各个方面都有不同程度的变化,追踪调查研究这些变化可认识县改市后的小城市城市化发展与婚姻家庭相互影响的关系,这对整个社会的全面和谐可持续发展颇有价值。     

Disease clusters: should they be investigated, and, if so, when and how?

Individual cluster reports are subject to several difficulties in interpretation. Although they rarely lead to new aetiological insights, a public health response to delineate the size and extent of any excess risk may be warranted. Further investigation, where merited, should usually include an examination of data for different areas and/or different time periods. A statistical evaluation of disease clusters is often secondary to a detailed appreciation of issues such as the availability and quality of data, confounding and bias in the selection of areas for study.     

Missing data methods for arbitrary missingness with small samples

Missing data are a prevalent and widespread data analytic issue and previous studies have performed simulations to compare the performance of missing data methods in various contexts and for various models; however, one such context that has yet to receive much attention in the literature is the handling of missing data with small samples, particularly when the missingness is arbitrary. Prior studies have either compared methods for small samples with monotone missingness commonly found in longitudinal studies or have investigated the performance of a single method to handle arbitrary missingness with small samples but studies have yet to compare the relative performance of commonly implemented missing data methods for small samples with arbitrary missingness. This study conducts a simulation study to compare and assess the small sample performance of maximum likelihood, listwise deletion, joint multiple imputation, and fully conditional specification multiple imputation for a single-level regression model with a continuous outcome. Results showed that, provided assumptions are met, joint multiple imputation unanimously performed best of the methods examined in the conditions under study.     

Numerical Comparison Between Different Empirical Prediction Intervals Under the Fay-Herriot Model

Recently, an empirical best linear unbiased predictor is widely used as a practical approach to small area inference. It is also of interest to construct empirical prediction intervals. However, we do not know which method should be used from among the several existing prediction intervals. In this article, we first obtain an empirical prediction interval by using the residual maximum likelihood method for estimating unknown model variance parameters. Then we compare the later with other intervals with the residual maximum likelihood method. Additionally, some different parametric bootstrap methods for constructing empirical prediction intervals are also compared in a simulation study.     

中小企业应用电子数据交换（EDI）的利与弊

全球经济一体化的环境下，EDI使得国际贸易成本低而高效。投身于国际贸易的中小企业需要考虑实际情况应用EDI     

Specification Analysis in Dynamic Models

The theoretical and empirical implications of omitted variables, particularly dynamic adjustment effects, are studied. In particular, the attempt to model for such omissions by including possibly irrelevant variables is investigated. This extends the existing knowledge of misspecification analysis in several directions. Ordinary least squares is the estimation technique under study, as has been the case in several recent and related studies. In our empirical example, the question of seasonal variation in interest rates is addressed. We deal with the related issue of deterministic versus stochastic detrending and demonstrate that it can be usefully cast in the context of “misspecification analysis” in dynamic models developed in this article.     

Bootstrap mean squared error of a small-area EBLUP

Concerning the estimation of linear parameters in small areas, a nested-error regression model is assumed for the values of the target variable in the units of a finite population. Then, a bootstrap procedure is proposed for estimating the mean squared error (MSE) of the EBLUP under the finite population setup. The consistency of the bootstrap procedure is studied, and a simulation experiment is carried out in order to compare the performance of two different bootstrap estimators with the approximation given by Prasad and Rao [Prasad, N.G.N. and Rao, J.N.K., 1990, The estimation of the mean squared error of small-area estimators. Journal of the American Statistical Association, 85, 163–171.]. In the numerical results, one of the bootstrap estimators shows a better bias behavior than the Prasad–Rao approximation for some of the small areas and not much worse in any case. Further, it shows less MSE in situations of moderate heteroscedasticity and under mispecification of the error distribution as normal when the true distribution is logistic or Gumbel. The proposed bootstrap method can be applied to more general types of parameters (linear of not) and predictors.