Evaluating technical efficiency of human capital formation in the Italian university: Evidence from Florence

This paper models the human capital formation in the Italian university and utilizes a measure of technical efficiency to estimate the output-efficiency of human capital formation in the University of Florence, by using Data Envelopment Analysis (DEA) on a selected set of inputs and outputs. It uses the Program Evaluation (PE) procedure as well, in an attempt to attribute shares of the variation in efficiency to factors that are beyond the control and factors that are under the control of the graduates and faculties. The authors thank two anonymous referees for their valuable comments.     

农民专业合作社成员参与行为、效果及作用机理

农民专业合作社成员参与行为包含了业务参与、资本参与和管理参与三维度,成员的业务和资本参与能使成员获得不错的经济收益,而成员管理参与是维护成员在合作社中合法收益的制度保障。依托路径分析方法,实证分析研究结果支持成员收益程度更加取决于成员的业务和资本参与水平,成员满意程度更加依托于成员的管理参与水平,研究结果也支持了股东成员更加积极进行管理参与。为推进农民专业合作社的规范可持续发展,促进成员增收致富等,需要考虑鼓励成员进行资本、业务和管理三维度的全面参与,限定单个成员最高入股比例,多途径吸引成员进行产品惠顾;鼓励成员参与成员大会、进行重大事项投票等,并正视成员的非经济期望与需求。     

中国2004年前11个月经济运行情况和2005年展望

文章对2004年前11个月经济运行的基本情况和本轮宏观调控的成效进行了阐述,并从定性和定量两个角度对2005年经济增长率和物价上涨率进行了分析和预测,最后对2005年宏观经济调控提出了一些政策性建议。     

Electronic Data Interchange: Research Review and Future Directions*

For nearly two decades, electronic data interchange (EDI) has been widely viewed as a technology pivotal to supply chain management that has also provided benefits to firms on multiple levels. Despite a substantial body of literature, there are a number of conflicting and inconclusive research results in this field. In this study, we synthesize the diverse body of research in EDI by organizing the literature into an initial theoretical framework. Based on a meta‐analysis of results from the empirical literature, we seek to clarify conflicting results from the literature in order to develop a more unified theoretical framework of contextual variables associated with EDI adoption factors and outcomes. From a managerial standpoint, our literature‐based framework offers a set of guidelines for making successful EDI adoption and implementation decisions.     

Akaike Information Criterion for Selecting Variables in the Nested Error Regression Model

The Akaike Information Criterion (AIC) is developed for selecting the variables of the nested error regression model where an unobservable random effect is present. Using the idea of decomposing the likelihood into two parts of “within” and “between” analysis of variance, we derive the AIC when the number of groups is large and the ratio of the variances of the random effects and the random errors is an unknown parameter. The proposed AIC is compared, using simulation, with Mallows' C _p , Akaike's AIC, and Sugiura's exact AIC. Based on the rates of selecting the true model, it is shown that the proposed AIC performs better.     

Decision and choice in transforming data

Statistics are developed for predicting the effect of data transformations on the F statistic when the assumptions of homoscedasticity and normality underlying the AN OVA are not necessarily satisfied. These statistics are useful for determining whether and how to transform, They are developed by partitioning the change in the observed value of the jF-statistic under the transformation, into two expressions, one of which depends on the "truth" of HQ while the other does not. Using this partition, desirable properties are derived for transformations. Criteria are developed defining transformations which tend to preserve the type 1 error while increasing power when needed. Using these criteria, the notion of model robustness is introduced. It is shown that the Box-Cox methodology for selecting a power transform may, under certain conditions, produce a transformation which does not permit inferences to be made about the parent population from the transformed population. An alternative approach suggested here does permit such inferences.     

An extension of the median test to analysis of variance

In a k-way analysis of variance model, the major concern is testing for main effects and for the presence of interaction between the factors. When the assumptions of normality and equal variances are satisfied, the appropriate test to use is the usual F-test for ANOVA. However, when the normality assumption is not satisfied then a robust or nonparametric test is needed to conduct the analysis. In this paper a nonparametric method based on cell counts is proposed. Each cell is divided into L subcells based on predetermined outpoints and the resulting frequencies are laid out in a contingency table. Then the Pearson x² and tne likelihood ratio tests are performed. A comparison with the classical ANOVA F-test indicates that the proposed method is preferable when the data comes from a thick-tailed highly skewed distribution.     

Sliced inverse regression under linear constraints

We consider the semiparametric regression model introduced by Li (1991) and add to this model some linear constraints on the slope parameters. These constraints can be identifiability conditions or they may carry additional in¬formations on the slope parameters. Using a geometric argument, we develop a method to estimate the slope parameters. This link-free and distribution-free method splits in two steps: the first is a Sliced Inverse Regression (SIR); Canonical Analysis is used at the second step to transform the SIR estimates so that they satisfy the constraints. We establish yn-consistency and obtain the asymptotic distribution of the estimates.

This estimation method is applied to the general sample selection model which is very useful in Econometrics. A simulation study shows that the method performs well in the example considered.     

On Energy Policy Models

Many energy models cannot be relied upon in forecasting or policy analysis. The quality of the data is often poor, and the theoretical underpinnings tend to be inadequate. These points are illustrated by example.     

A bayesian analysis of trend determination in economic time series

In this paper we provide a comprehensive Bayesian posterior analysis of trend determination in general autoregressive models. Multiple lag autoregressive models with fitted drifts and time trends as well as models that allow for certain types of structural change in the deterministic components are considered. We utilize a modified information matrix-based prior that accommodates stochastic nonstationarity, takes into account the interactions between long-run and short-run dynamics and controls the degree of stochastic nonstationarity permitted. We derive analytic posterior densities for all of the trend determining parameters via the Laplace approximation to multivariate integrals. We also address the sampling properties of our posteriors under alternative data generating processes by simulation methods. We apply our Bayesian techniques to the Nelson-Plosser macroeconomic data and various stock price and dividend data. Contrary to DeJong and Whiteman (1989a,b,c), we do not find that the data overwhelmingly favor the existence of deterministic trends over stochastic trends. In addition, we find evidence supporting Perron's (1989) view that some of the Nelson and Plosser data are best construed as trend stationary with a change in the trend function occurring at 1929.