存在自相关时自回归模型的估计和检验

讨论存在自相关情况下自回归模型中随机解释变量的内生性,指出目前的计量经济理论所存在的问题,证明了随机误差存在自相关情况下一阶自回归模型和高阶自回归模型的随机解释变量与随机误差都不相关,同时改进了自回归模型的估计和检验方法。     

中国经济增长空间相关性研究——基于"长三角"与"珠三角"的实证

本文建立了两套回归模型:无空间相关变量的单向和双向固定效应模型;有空间相关变量的单向和双向固定效应模型。对比分析后得出的结论有:"长三角"和"珠三角"区域内部的确存在经济增长的空间相关性,"珠三角"区域内部经济增长的空间相关性略大于"长三角"区域,但两者在统计上并没有显著的差别;两个地区都存在经济增长的β收敛,但"珠三角"地区呈现出更强的收敛性;对于影响"长三角"和"珠三角"经济增长的因素,有些相同和不同点值得关注。     

我国经济增长中劳动力贡献率变化的实证分析

文章依据新古典经济增长模型，通过引入虚拟变量，发现我国从改革开放至今，物质资本积累是经济增长的主要动力，经济增长方式为粗放型，但同时也发现劳动力作为经济增长的另一车轮，其对经济增长的贡献率发生了较大的增长，经济增长方式正在向集约型方向转变。     

基于LVQ的上证综合指数择时理论研究

利用学习向量量化（LVQ，Learning Vector Quantization）神经网络，构建上证综合指数择时模型；结合技术，分析了经济基本面的变化；并在模型的输入变量中，对经济数据以及与股市本身相关的变量进行预测；进而通过判断上证综合指数的涨跌，采取对应的进入、退出策略，以期得到优于指数的超额收益。研究结果表明，相对于人工神经网络模型， LVQ模型能够较好地对进行模型识别，是一种比较理想的预测模型。     

VAR宏观计量经济模型的演变与最新发展——基于2011年诺贝尔经济学奖得主Smis研究成果的拓展脉络

本文对VAR宏观计量经济模型的拓展和应用进行综述和总结。VAR模型在批判中逐步向结构式、非线性、空间计量以及利用贝叶斯统计推断技术的方向演变。进入21世纪之后,基于动态随机一般均衡的DSGE-VAR模型、时变参数的TVP-VAR模型以及处理大规模变量的FAVAR模型等成为宏观计量经济分析的前沿。本文最后指出该类模型在运用中应注意的问题。     

教育投资会显著促进经济增长吗?——基于向量自回归模型的实证研究

本文基于1992年-2012年的中国教育经费投入和经济增长的数据,利用向量自回归模型探讨了教育投资对经济增长的影响。除考虑教育投资外,我们还引入了消费、企业投资、政府投资以及净出口对经济增长的影响。经过检验我们发现:相对于其他参考变量,教育投资对于经济增长有更明显的拉动作用,并且对经济增长的影响较为持续。最后,根据实证结果,本文对教育投资提出了政策建议。     

中国经济增长与能源消费空间面板分析

近年来面板数据广泛应用于各类统计分析,但变量的空间相关并没有引起足够的重视。本文引入空间面板回归模型研究中国各省市区经济增长和能源消费的关系。利用matlab软件及其spatial econometric模块建立和比较传统面板回归模型与空间面板回归模型,结果表明空间面板回归模型较传统面板回归模型优越,本文的研究为空间面板数据分析提供了一个应用实例。     

我国宏观经济运行动态模拟与预测

一、研究的意义。监测我国宏观经济运行状况是否良好。经济是否能持续、快速、稳定、健康地发展。一直是我国学和政府时刻关注的焦点。而这一评价过程又是通过运用诸多宏观经济指标及指标间的联系来衡量的。在以往大多数研究中，是通过逐个建立单个指标对其他一个或多个指标相应的单一方程模型，对未来有关宏观经济指标进行预测。但是单方程模型有一个隐含的假定，就是被解释变量与解释变量之间如果有因果关系。则这种关系是单向的，即解释变量是原因，被解释变量是结果。单方程模型只是从一个方向解释了变量间的因果关系，因变量与解释变量之间没有反馈式的关系。但是在国民经济运行中，根据这种单向因果关系建立模型进行预测几乎是没有意义的。因为解释变量与被解释变量之间有着双向或联立关系，这时就需要采用联立方程模型．它可以使我们考虑多个变量之间的相互关系．通常这类模型由一组回归方程构成。这样，在估计一个方程的参数时就可以运用方程组中其他方程所提供的信息。我们可以运用一些统计软件来对这些方程进行估计。本采用计量经济学软件Eviews 3．1来对联立方程进行估计。在单方程模型里。普通最小二乘法是最适合这种模型的．但是两个或多个内生变量的同时存在需要我们使用另外的建模和估计方法。因为方程的联立性会使普通最小二乘法得到的参数估计量不一致。因为在联立方程模型中，一个方程中的内生变量往往又影响另一个方程中的其他变量，因此误差项与内生变量相关，所以普通最小二乘估计量也将是有偏和不一致的。     

就业与物价通论——“收入(就业)与物价决定论”和“广义的乘数原理”

五、“收入(就业)与物价决定论”模型(“IS-LM-LD”模型) 线索在西方国家的“混合经济”体制下,政府采用宏观的财政政策和货币政策对经济进行干预(西方国家不常使用的收入政策,这里暂时不谈,放在后面讨论)。因此,在描述一国经济的完整的宏观经济系统模型中,外生变量只能是财政政策变量、货币政策变量及对经济有影响的环境因素,物价水平在经济系统的运行中形成,因而应当是模型的内生变量。但是,从图18(见本刊1990年第11期)可以看到,“IS-LM”模型中,物价水平P是外生变量,因此,“IS-LM”模型显然不是完整的宏观经济系统模型,必须对它加     

关于正确使用虚拟变量的探讨

虚拟变量是表示属性的变量,使用到经济计量模型中,应该表示重要的属性。但是在我国目前的一些宏观经济模型中,虚拟变量被使用得太多了。我国处于改革开放的过程中,不时会有一些特殊原因对经济变量产生正面或负面影响,因此,模型作者很容易为其被解释变量中的离群数据找到理由,在解释变量中加入相应的虚拟变量,拟合出精度很高的模型。但是,这样构造的模型,拟合精度越高,预测精度也就越差,因为我们不知道未来会有甚么“特殊原     

Polytomous logistic regression

In this paper a review will be given of some methods available for modelling relationships between categorical response variables and explanatory variables. These methods are all classed under the name polytomous logistic regression (PLR). Models for PLR will be presented and compared; model parameters will be tested and estimated by weighted least squares and by likelihood. Usually, software is needed for computation, and available statistical software is reported.
An industrial problem is solved to some extent as an example to illustrate the use of PLR. The paper is concluded by a discussion on the various PLR-methods and some topics that need a further study are mentioned.     

Tests and uses of macro-econometric models: A critical survey

Summary In spite of some recent developments, the general area of methods for testing and using macro-econometric models is in a very underdeveloped state. Statistical measures like Theil's inequality coefficient or simple mean square error give some idea about the performance of forecasts of individual variables. They do not give a satisfactory measure for the model as a whole. Even more important, these statistical measures do not provide a proper economic measure of the efficiency of the model. In general we would like to have some measure of the sensitivity of optimum policy developed on the basis of the model. The acceptability of the model will then depend on whether the degree of uncertainty thus associated with the objective function of the policy maker is acceptable. This of course requires specification of the objective function of the authorities using the macro-econometric models. Here again the progress achieved so far is rather limited. We have argued for a programming approach to the problem where instead of searching for a general welfare function, we accept one target as the object of maximisation while other targets appear as constraints. This approach however, is only a shortrun solution. The longer run problem of specifying a social welfare function and evaluating and using macro-econometric models has so far been barely scratched on surface. The author is grateful to Professor J. A. Sawyer for his valuable comments at various stages of this paper. This survey was done in connection with the project on Econometric Model of the U.K. that is going on at London Business School under the supervision of Professor R. J. Ball.     

中国税制结构和宏观税负影响因素分析——基于时间序列数据的分析

中国税制改革必然涉及到税制结构的调整和宏观税负的变动。根据设定的模型,文章对1994年-2008年中国税制结构和宏观税负影响因素进行了分析,发现有的经济变量既影响税制结构又影响宏观税负,而有的变量仅影响税制结构,对宏观税负则没有影响。     

论我国审计风险模型的重构——基于国际审计风险模型的演变分析

国际会计师联合会(IFAC)的国际审计与保证准则委员会(IAASB)为了提高审计质量,发布了一系列新准则,其中ISA200准则对审计风险模型进行了重大改动。本文主要通过对国际审计风险模型的演变进行分析,进而对我国审计风险模型的重构提出了一些断想。     

Measurement of innovation efficiency in logistic enterprises: Evidence from China based on the three-stage DEA-Malmquist index model approach

Continuous improvement of technological innovation ability, adjustment of the development strategy, and enhancement of operational performance are of great theoretical and practical significance for logistics enterprises. This paper aims to analyze and evaluate the innovation efficiency of the logistics industry. The study utilizes the static three-stage DEA model and the dynamic Malmquist index model, considering a total of 12 indicators related to innovation input, output, and environmental variables. A dataset of 2940 entries from 49 listed logistics enterprises from 2017 to 2021 was calculated. The analysis provides insights into the innovation efficiency of logistics enterprises from a static perspective and the innovation total factor productivity from a dynamic perspective and decomposition terms. Based on the analysis of environmental variables by the SFA model, it was found that DEA inefficiency is the combined result of environmental factors and management inefficiency. Environmental variables have both positive and negative effects on innovation. The improvement of the economic development level will lead to excess R&D investment. Increased government simple fund subsidies are not conducive to the efficient allocation of innovation resources within enterprises. The expansion of enterprise scale will increase R&D personnel and investment in fixed assets. A thriving technology market can encourage enterprises to improve their own conversion rate of scientific and technological output and give full play to their innovation ability. The dynamic Malmquist model analysis reveals a recution in the overall innovation efficiency of listed logistics enterprises over 5 years. The changes in total factor productivity and technological progress efficiency of all listed logistics enterprises are synchronized, with most enterprises exhibiting higher technological progress efficiency compared to comprehensive technical efficiency. The total factor productivity of logistics enterprise innovation is mainly affected by comprehensive technical efficiency.     

Analytic derivation of sociometric dimensions

Dimensional analysis has been defined variously as the synthesis of dimensionless variables and as a way of spatially representing data. While in sociometrics, one may not be able to devise models resembling those of the physical sciences because of the complexity of many social situations, one may in some cases successfully apply the techniques of classic dimensional analysis and get empirically valid results. More often we derive with analytic methods from the social sciences different kinds of dimensional frameworks in which to represent data. This paper will compare these approaches to the study of relationships and give examples of where each might be appropriate. Finally, a classification of the analytical methods will be presented.     

Consistent selection of explanatory variables

We consider a linear regression model where some explanatory variables are unknown members of sets of alternative explanatory variables. It will be shown that under weak conditions the minimum residual variance criterion for selecting these explanatory variables has the property that the probability of selecting wrong explanatory variables vanishes if the number of observations increases to infmity. Moreover, the O.L.S. estimator of the resulting "specified" model turns out to be consistent, while in the case that all the parameters are nonzero it can be shown that this O.L.S. estimator has the same limiting distribution as the O.L.S. estimator of the true model.     

Solving Multicollinearity in the Process of Fitting Regression Model Using the Nested Estimate Procedure

In the practical cases, we are usually faced with the more difficult problem of multicollinearity in our fitted regression model. Multicollinearity will arise when there are approximate linear relationships between two or more independent variables. It may cause some serious problems in validation, interpretation, and analysis of the model, such as unstable estimates, unreasonable sing, high-standard errors, and so on. Although there are some methods to solve or avoid this problem, we will propose another alternative from the practical view in this paper, called nested estimate procedure. The first half of the paper explains the concept and process of this procedure, and the second half provides two examples to illustrate this procedure’s suitability and reliability.     

Effects of Using Mean Scores in Regression Models: An Example From Environmental Psychology

Multiple regression analysis with grouped data is often used as a method for exploring environmental preferences, the preferred unit for measurement in such analyses is mean scores rather than individual scores.Although this procedure allows us to reduce the potential for error in measuring different variables and, as a consequence of this, improves the reliability of the technique, it also produces some additional, undesirable effects. The latter include artificial increases in R² values which give the impression that a high degree of fit has been achieved for the regression model. Indeed, this goodness fit often appears to be better than that which could have been achieved by using individual scores. Further, given that different studies operate with differing numbers of subjects in their groups, the R² scores which result from the analyses of these groups are not directly comparable.In the following discussion, we demonstrate how any value, other than zero for correlations between variables, can be increased, at will, by simply expanding the number of subjects in each group. We present the specialised formulae used for quantifying this increase and offer a warning about the purely relative nature of any study which bases its conclusions on models of regression analysis using grouped data.