考虑能源投入的生产函数模型及实证

本文构建了考虑能源消耗作为投入的生产函数模型,并结合面板数据模型研究了能耗对于我国钢铁企业产出的影响。研究结果表明,能源消耗的影响占据了第2位,说明研究系统效率时考虑能源消耗的影响是非常必要的,可以为决策提供依据。     

CGE模型中CES生产函数的贝叶斯估计

文章讨论了如何把贝叶斯统计方法应用到单个方程非线性计量经济学模型中。首先对贝叶斯统计方法进行了分析;然后以CES生产函数模型为例分析了单个方程非线性模型的贝叶斯估计问题;最后分析了常替代弹性（CES）产函数模型在不同的先验分布下的贝叶斯估计。     

基于边际报酬递减规律的短期生产函数模型

在短期,生产过程受到生产要素边际报酬递减规律的支配,而传统的生产函数模型无法完整地表现边际报酬递减规律的整个运动过程.针对这一问题,文章从边际报酬递减规律出发,定量分析了短期生产函数和F分布密度函数的图像特征的相似性,并以F分布密度函数为基础函数,构造了符合边际报酬递减规律的短期生产函数模型.构造生产函数模型为定量研究边际报酬的运动规律,确定生产要素最佳投入,探索生产要素最佳配置提供一些方法借鉴.     

多水平C-D生产函数模型及其参数异质性研究

文章利用多水平模型分析方法建立了多水平C-D生产函数模型。该模型考虑了层次结构及异质性对C-D生产函数的影响,并基于我国1997~2007年经济发展相关数据进行了实证建模和数据分析。结果表明,多水平模型能够更好地反映我国经济发展规律,同时发现其资本贡献率显著依赖地区对外开放度,从而揭示了生产函数模型的参数异质性特征。文章还通过拟合模型,估算了各省份资本要素的贡献份额及其与对外开放度的关系。     

基于非参数DEA前沿的参数生产函数估计模型

本文结合参数方法和非参数方法的估计模型的方法对确定前沿生产函数进行估计 ,并从理论上验证了两者结合的估计方法的强收敛性 ,并结合实证结果证明了两种方法结合的合理性和广阔前景。　　 一、生产函数估计方法前沿生产函数作为经济系统最优行为组合的有效边界 ,从理沦上讲是     

基于贝叶斯面板模型的扩展C-D生产函数估计及实证

利用2008—2011年的省际面板数据并充分收集和整理先验信息,运用贝叶斯面板模型分析和研究了扩展的C-D生产函数,有效地处理了短期时序造成的小样本问题以及误差项之间的相关性问题,实现参数的适时修正和更新。研究发现:目前中国物质资本份额的后验均值为55%,明显低于中国学者估计的60%;人力资本所占份额仅为5%~6%,其对经济增长的拉动作用没有得到充分的发挥,中国应增大人力资本投入;固定资产投资比例人均产出弹性的后验均值为1.37;地理位置等个体差异依旧是造成中国发展不平衡问题逐年扩大的重要原因。     

基于线性模型的生猪生产数据调整及其波动特征研究

客观准确的数据是揭示生猪生产波动规律的首要条件。第二次全国农业普查后,官方对部分年份的生猪生产数据进行了调整,分析发现:所调整数据的虚报率符合线性变化规律,故在此基础上构建线性修正模型,对1985—1995年生猪生产数据进行调整,并利用调整后的数据对中国生猪生产波动特征进行分析,得出三点结论:第一,20世纪80年代前后相比较,生猪生产平均波动周期长度有所增加;第二,生猪生产的收缩期长度和收缩强度均大于扩张期的相应特征,即中国生猪生产波动的下滑特征更为明显;第三,生猪生产波动幅度呈现U型变化趋势,且现阶段正处于波动幅度不断加剧的过程。因此,有必要继续加强对生猪生产波动规律的探寻和监测,为缓解中国生猪生产波动提供可靠依据。     

生产函数在统计中的应用

1928年柯布—道格拉斯(Cobb—Douglas)生产函数问世以来,对生产函数的研究不断深入。1957年索洛(Solow)在技术进步为希克斯(Hicks)中性的假设条件下,将生产函数写为     

超越对数生产函数的半参数变系数估计模型

提出超越对数生产函数的半参数变系数模型,利用Profile方法给出产出弹性函数系数的局部加权最小二乘估计,并利用非参数条件自助法对有限样本的近似分布进行模拟,给出相对精确的广义似然比检验。规模报酬约束下中国1953--2008年的实证结果拒绝超越对数生产函数模型假设,产出弹性不可简单线性化而是对数劳均资本的非线性函数,时变资本弹性表现为倒U型变化趋势,时变劳动力弹性表现为U型变化趋势。     

基于时空异质弹性生产函数模型的区域全要素生产率再测度

基于中国要素份额时变和要素禀赋存在空间差异的经济事实,以新古典生产函数为基础,构建包含时间因素和空间因素的时空异质弹性生产函数模型,并对不同省份全要素生产率及其动态演进趋势进行分析。研究表明:在要素产出弹性方面,改革开放以来中国要素产出弹性存在较为明显的时空差异性,现阶段大多数省份资本和劳动力产出弹性逐渐稳定在0.6和0.4左右;在全要素生产率测度方面,中国全要素生产率增长率以1992年为时间节点,前后呈现出不同的波动特征,1992年之后波动幅度开始减小。     

Parameter estimation of a composite production function model based on improved artificial fish swarm algorithm and model application

The traditional Cobb–Douglas production function uses the compact mathematical form to describe the relationship between the production results and production factors in the production technology process. However, in macro-economic production, multi-structured production exists universally. In order to better demonstrate such input–output relation, a composite production function model is proposed in this article. In aspect of model parameter estimation, artificial fish swarm algorithm is applied. The algorithm has satisfactory performance in overcoming local extreme value and acquiring global extreme value. Moreover, realization of the algorithm does not need the gradient value of the objective function. For this reason, it is adaptive to searching space. Through the improved artificial fish swarm algorithm, convergence rate and precision are both considerably improved. In aspect of model application, the composite production function model is mainly used to calculate economic growth factor contribution rate. In this article, a relatively more accurate calculating method is proposed. In the end, empirical analysis on economic growth contribution rate of China is implemented.     

Dimension reduction transfer function model

The dimension reduction in regression is an efficient method of overcoming the curse of dimensionality in non-parametric regression. Motivated by recent developments for dimension reduction in time series, an empirical extension of central mean subspace in time series to a single-input transfer function model is performed in this paper. Here, we use central mean subspace as a tool of dimension reduction for bivariate time series in the case when the dimension and lag are known and estimate the central mean subspace through the Nadaraya–Watson kernel smoother. Furthermore, we develop a data-dependent approach based on a modified Schwarz Bayesian criterion to estimate the unknown dimension and lag. Finally, we show that the approach in bivariate time series works well using an expository demonstration, two simulations, and a real data analysis such as El Niño and fish Population.     

Estimating the survival function based on the semi-Markov model for dependent censoring

In this paper, we study a nonparametric maximum likelihood estimator (NPMLE) of the survival function based on a semi-Markov model under dependent censoring. We show that the NPMLE is asymptotically normal and achieves asymptotic nonparametric efficiency. We also provide a uniformly consistent estimator of the corresponding asymptotic covariance function based on an information operator. The finite-sample performance of the proposed NPMLE is examined with simulation studies, which show that the NPMLE has smaller mean squared error than the existing estimators and its corresponding pointwise confidence intervals have reasonable coverages. A real example is also presented.     

Effects of non-normality on the power function in a one-way random model

The effects of non-normality on type-I and type-II errors in a one-way random model are investigated for moderate departures from normality. It is found that the probabilities of both errors are more sensitive to the kurtosis of between group effects than that of within group effects.     

Automatic model identification using vector sample autocorrelation function

One of the weaknesses of the ESACF approach for model identification by Tsay and Tiao(1984) is the ambiguity which can be caused by the elements which are in triangle but marginally larger than two standard deviation values. To avoid this drawback, a vector sample autocorrelaton function (VSACF) is defined and an automatic model identification procedure using the VSACF is developed. We illustrate this approach with four examples.     

Testing the validity of the logistic model based on the empirical distribution function

The logistic distribution is one of the fundamental distribution and is widely used for describing model growth curves in survival analysis and biological studies. Applications of this distribution are presented in statistical literature. In this article, goodness of fit tests for the logistic distribution based on the empirical distribution function (EDF) are considered. In order to compute the test statistics, because the MLEs cannot be obtained explicitly, we use the approximate maximum likelihood estimates (AMLEs) suggested by Balakrishnan and Cohen (1990 Balakrishnan, N., Cohen, A. C. (1990). Order Statistics and Inference: Estimation Methods. Boston: Academic Press. [Google Scholar]), which are simple explicit estimators. Power comparisons of the considered tests are carried out via simulations. Finally, two illustrative examples are presented and analyzed.     

Conditional time-dependent nonparametric estimators with an application to healthcare production function

ABSTRACT

By using the probabilistic framework of production efficiency, the paper develops time-dependent conditional efficiency estimators performing a non-parametric frontier analysis. Specifically, by applying both full and quantile (robust) time-dependent conditional estimators, it models the dynamic effect of health expenditure on countries’ technological change and technological catch-up levels. The results from the application reveal that the effect of per capita health expenditure on countries’ technological change and technological catch-up is nonlinear and is subject to countries’ specific income levels.     

A software reliability model using mean residual quantile function

In this paper, we propose a class of distributions with the inverse linear mean residual quantile function. The distributional properties of the family of distributions are studied. We then discuss the reliability characteristics of the family of distributions. Some characterizations of the class of distributions are also discussed. The parameters of the class of distributions are estimated using the method of L-moments. The proposed class of distributions is applied to a real data set.     

The univariate distribution function for a particular bilinear model

The aim of the paper is to find the univariate stationary distribution of a particular bilinear process. In this context, we propose a novel approach to derive the distribution function. It is based on a recursive formula and allows to relax the conditions on the moments of the process. We also show that the derived approximation converges to the true distribution function. The accuracy of the recursive formula is evaluated for finite sample dimensions by a small simulation study.Received: February 2003, Revised: May 2004,     

Flexibility and regularity properties of the asymptotically ideal production model

This paper uses simulations to investigate the ability of the Asymptotically Ideal Model(AIM) to approximate unknown technologies. AIM was proposed by Barnett and Jonas (1983) as an expansion which could approximate any indirect utility or cost function arbitrarily well at all prices. By applying AIM to simulated data, we fmd a significant improvement in prediction by higher orders of AIM relative to the generalized Leontief and the translog. The largest error in approximating some CES technologies with AIM is 125 times smaller than that of the generalized Leontief and we find a translog approximation error 1000 times that of AIM in others. We also fmd that non-negativity constraints used by Barnett, Geweke, and Wolfe (1991) to impose concavity on the model greatly restrict the flexibility of AIM, even when all the inputs are substitutes.