中国各地区市场化进程区位分布的空间效应研究

 本文从空间分析的角度研究各地区市场化进程的空间效应及其对区域经济增长绩效的影响,空间计量经济分析的结果表明市场化进程的空间分布在整个样本区间与GDP、资本和劳动一样呈现出集聚的趋势,区域差异显著,各地区的市场化进程在地理空间上存在显著的自相关关系,证实了各地区市场化进程存在制度溢出效应。因此,在研究各地区市场化进程与经济增长关系时必须考虑其空间效应的影响,基于此结论本文运用空间计量模型测算了各地区市场化进程对经济增长的贡献。结果证实,考虑了空间效应后,市场化进程在1998～2001年阶段对经济增长的贡献约为6.2％,在2002～2005年阶段约为10.8％,这表明了市场化对中国的经济增长起到了重要的作用,而且近年来的贡献愈加显著。     

A new nonlinear asymmetric cointegration approach using error correction models

In this article, two new powerful tests for cointegration are proposed. The general idea is based on an intuitively appealing extension of the traditional, rather restrictive cointegration concept. In this article, we allow for a nonlinear, but most importantly a different, asymmetric convergence process to account for negative and positive changes in our cointegration approach. Using Monte Carlo simulations we verify, that the estimated size of the first test depends on the unknown value of a signal-to-noise ratio q. However, our second test—which is based on the original ideas of Kanioura and Turner—is more successful and robust in the sense that it works in all of the different evaluated situations. Furthermore it is shown to be more powerful than the traditional residual based Enders and Siklos method. The new optimal test is also applied in an empirical example in order to test for potential nonlinear asymmetric price transmission effects on the Swedish power market. We find that there is a higher propensity for power retailers to rapidly and systematically increase their retail electricity prices subsequent to increases in Nordpool's wholesale prices, than there is for them to reduce their prices subsequent to a drop in wholesale spot prices.     

Comparison of methods for analyzing binary repeated measures data: A simulation-based study (comparison of methods for binary repeated measures)

In this study, some methods suggested for binary repeated measures, namely, Weighted Least Squares (WLS), Generalized Estimating Equations (GEE), and Generalized Linear Mixed Models (GLMM) are compared with respect to power, type 1 error, and properties of estimates. The results indicate that with adequate sample size, no missing data, the only covariate being time effect, and a relatively limited number of time points, the WLS method performs well. The GEE approach performs well only for large sample sizes. The GLMM method is satisfactory with respect to type I error, but its estimates have poorer properties than the other methods.     

Characterization of Unacceptable Solutions in LP Discriminant Analysis*

Many linear programming models have been proposed for performing discriminant analysis. Partial characterizations for unacceptable solutions have been presented and new models proposed to circumvent these problems. In this paper those conditions leading to unacceptable solutions for all two-group models are characterized.     

Modeling Synergy and Learning under Multiple Advanced Manufacturing Technologies

The advent of the wide variety of new, highly integrated, advanced manufacturing technologies available for acquisition by a firm's managers has brought to light an accompanying set of unexpected issues. These issues include expectations for the benefits and costs of these technologies, determining the appropriate order of implementation and finding a way to justify acquisition when many of the benefits are a function of the technology's learning and synergistic effects on other operations and technologies. We present here a model that captures the interaction effects of these highly integrated technologies and discuss the data requirements for application of the construct. We then illustrate the model's workings with a number of multitechnology examples and show the danger of ignoring the synergistic and learning effects of these technologies when considering their acquisition.     

Inference in a Binary Response Model with Applications to Data Analysis*

Standard errors of the coefficients of a logistic regression (a binary response model) based on the asymptotic formula are compared to those obtained from the bootstrap through Monte Carlo simulations. The computer intensive bootstrap method, a nonparametric alternative to the asymptotic estimate, overestimates the true value of the standard errors while the asymptotic formula underestimates it. However, for small samples the bootstrap estimates are substantially closer to the true value than their counterpart derived from the asymptotic formula. The methodology is discussed using two illustrative data sets. The first example deals with a logistic model explaining the log-odds of passing the ERA amendment by the 1982 deadline as a function of percent of women legislators and the percent vote for Reagan. In the second example, the probability that an ingot is ready to roll is modelled using heating time and soaking time as explanatory variables. The results agree with those obtained from the simulations. The value of the study to better decision making through accurate statistical inference is discussed.     

A Variable Selection Criterion in the Linear Programming Approaches to Discriminant Analysis*

There are numerous variable selection rules in classical discriminant analysis. These rules enable a researcher to distinguish significant variables from nonsignificant ones and thus provide a parsimonious classification model based solely on significant variables. Prominent among such rules are the forward and backward stepwise variable selection criteria employed in statistical software packages such as Statistical Package for the Social Sciences and BMDP Statistical Software. No such criterion currently exists for linear programming (LP) approaches to discriminant analysis. In this paper, a criterion is developed to distinguish significant from nonsignificant variables for use in LP models. This criterion is based on the “jackknife” methodology. Examples are presented to illustrate implementation of the proposed criterion.     

出口贸易与省域TFP空间维度分析

作为衡量一国科技进步的重要指标,全要素生产率的高低决定着一国生产率水平的先进与落后。在不断深化改革开放的过程中,如何利用开放倒逼我国技术水平的提高成为一项较为紧迫的任务。为此,我们运用面板DEA-Malmquist指数法可以测算出2002—2011年我国29个省份(含自治区、直辖市)全要素生产率,并在此基础上建立空间面板数据模型(SPDM),实证分析出口贸易、基础设施等对我国省域全要素生产率及其空间溢出效应的影响。经研究发现,我国省域全要素生产率存在较为明显的空间自相关性,即出口贸易、基础设施等经济活动通过影响本省TFP,并通过空间溢出效应进一步将其作用传递和迭加到邻近省域全要素生产率上,基础设施对我国省域全要素存在较为显著的负向关系,出口依存度、出口贸易及FDI对我国省域全要素生产率具有显著的推动作用。     

An Empirical Comparison of Estimating Methods for the Ramberg–Osgood EIV Model

The standard tensile test is one of the most frequent tools performed for the evaluation of mechanical properties of metals. An empirical model proposed by Ramberg and Osgood fits the tensile test data using a nonlinear model for the strain in terms of the stress. It is an Error-In-Variables (EIV) model because of the uncertainty affecting both strain and stress measurement instruments. The SIMEX, a simulation-based method for the estimation of model parameters, is powerful in order to reduce bias due to the measurement error in EIV models. The plan of this article is the following. In Sec. 2, we introduce the Ramberg–Osgood model and another reparametrization according to different assumptions on the independent variable. In Sec. 3, there is a summary of SIMEX method for the case at hand. Section 4 is a comparison between SIMEX and others estimating methods in order to highlight the peculiarities of the different approaches. In the last section, there are some concluding remarks.