时变弹性生产函数模型统计学与经济学检验

 内容提要：本文给出时变弹性生产函数的半参数变系数Profile估计方法,提出利用统计学和经济学原理检验时变弹性生产函数显著性和准确性的新思路。中国实证研究发现,广义似然比统计检验无法拒绝 Cobb-Douglas生产函数,但是经济学检验拒绝Cobb-Douglas生产函数。时变弹性生产函数保留了Cobb- Douglas生产函数结构形式,具有明确的经济学意义,改进不变产出弹性中性技术进步假设,是更加符合实际的生产函数模型。     

要素市场扭曲、资本深化与产业结构调整——基于时变弹性生产函数的实证分析

基于时变弹性生产函数估计了中国资本要素1978—2009年分产业的产出弹性,用各年各产业的资本收入份额减去其产出弹性表示资本要素的扭曲程度,并实证研究了资本要素扭曲和资本深化的关系,结论表明:第一产业基本是负向扭曲,第二产业是不很规则的U型曲线,第三产业则基本是倒U型曲线;各个产业的资本深化程度不同,用资本产出比衡量,从大到小排列顺序为三二一;资本要素市场扭曲会促进产业资本深化,在第三产业尤为明显。政策层面既要发展第三产业,又要警惕其资本过度深化,根本出路在于要素市场完善与产业结构调整的市场化。     

要素投入结构、产出弹性与经济增长关系的统计检验

文章利用C-D生产函数,对2000-2013年山东省17地市三次产业的要素产出弹性进行了测算,并结合要素投入构成分析了山东省经济增长的结构动因与存在问题.研究发现:劳动、资本、技术研发三要素对山东省经济增长都产生了正向积极影响,但产出弹性数值和变化趋势相差较大,其中,资本产出弹性最高且表现平稳,劳动产出弹性明显低于资本产出弹性且表现出逐年下降的变化趋势,技术研发产出弹性最低,总体上,2000-2013年山东省经济增长除依赖劳动力增加与技术研发投入外,更大程度上主要依赖于资本投资的拉动.     

中国制造业劳动生产率增长来自全要素生产率变动还是要素积累效应——基于状态空间随机前沿面板模型的计量分析

基于超越对数生产函数构建具备时变截距项和时变斜率参数的随机趋势模型,采用卡尔曼滤波和卡尔曼平滑算法估计模型参数,并运用制造业劳动生产率增长分解方法分析制造业劳动生产率增长来源,识别全要要生产率变动、资本和中间投入要素积累效应对制造业劳动生产率增长的贡献。结果表明:资本投入和中间投入的一阶斜率参数具有显著的时变特征,且中间投入的一阶斜率参数远高于资本投入,各省中间投入要素积累效应对制造业劳动生产率增长的贡献远高于资本投入要素积累效应和全要素生产率变动,全要素生产率变动对制造业劳动生产率增长的相对重要性强于资本要素积累效应;由于资本和中间投入一阶系数显著时变上升,资本和中间投入的技术进步偏离成为全要素生产率变动的重要来源,且对制造业劳动生产率增长具有显著的正向影响。     

论超越对数生产函数要素替代弹性的逻辑错误

在运用超越对数生产函数模型进行实证研究的过程中,关于要素替代弹性的分析是重要的环节,必须建立在严谨的数理论证基础上。检索6篇涉及超越对数生产函数要素替代弹性的文献,对其论证过程进行验证,结果发现6篇文献的结论都不符合数理逻辑,进而提出了超越对数生产函数要素替代弹性的合理论证过程,并简要评述了超越对数生产函数要素替代弹性出现逻辑错误的原因、可能产生的不良后果以及防范的方法。     

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。     

中国要素替代弹性估计

要素替代弹性是经济研究中的重要参数,对经济增长和要素分配研究至关重要。本文基于一般要素增强型CES生产函数,利用1978-2011年省级面板数据,以变系数面板模型估计我国替代弹性时间序列。主要发现为：我国替代弹性明显小于1（0.23-0.55之间）,改革时期基本呈上升趋势;此时劳动增强型技术表现出资本偏向,构成近期我国劳动份额持续下降的重要原因。最后,对该领域未来发展方向进行展望。     

中国制造业创新技术进步要素偏向及其影响因素研究

现有研究对创新技术进步要素偏向关注甚少。本文基于超越对数生产函数的随机前沿分析方法测算了中国制造业部门创新技术进步的要素偏向指数,进而通过系统广义矩估计方法考察了创新要素结构、科研人员工资扭曲等因素对创新技术进步资本偏向的影响效果,我们发现：创新资本的产出弹性远远大于科研人员的产出弹性,制造业创新技术进步整体偏向于资本,且具有明显的路径依赖特征;创新投入要素结构的资本化程度、政府资助行为削弱了创新技术进步的资本偏向;科研人员工资扭曲、企业自有资金对创新活动的支持强化了资本偏向。优化创新方向、提高科研人员工资待遇对国家创新战略的实施至关重要。     

基于时变参数的中国经济增长因素分析

本文应用时变参数状态空间模型，估计了样本区间影响经济增长各因素的产出弹性，并计算得到各因素对经济的贡献率。研究发现，时变参数方法估计要素产出弹性的精确度很高；中国的经济增长方式仍为粗放型模式，促进科技进步，实现经济增长方式的转变，是中国经济长期稳定增长的必由之路。     

中国区域生产效率与经济发展差距研究

利用1998-2007年的省级面板数据,通过SFA模型利用超越对数函数估算了中国各省市区和三大区域历年的资本和劳动产出弹性、全要素生产率（TFP）水平及其效率变化,研究发现,东中西部的资本产出弹性大于劳动产出弹性。在此基础上对各省市区历年的全要素生产率水平进行了分析,结果表明,在1998-2007年间,东部的TFP增长率最高,中部地区高于西部地区。以上研究表明,TFP是造成区域社会经济发展差距扩大的主要因素之一。提高中西部全要素生产率生产效率和国家增加对中西部的投资可以缩小中国区域经济发展差异。     

半参数变系数回归模型的空间相关性检验

对半参数变系数回归模型,构造了新的空间相关性检验统计量,利用三阶矩 逼近方法导出了其检验 值的近似计算公式,蒙特卡罗模拟结果表明该统计量在检测空间相关性方面具有较高的准确性和可靠性。同时考察了误差项服从不同分布时的检验功效,体现出该检验方法的稳健性。进一步,我们还给出了检验统计量的Bootstrap方法以及检验水平的模拟效果。     

Estimation of general semi-parametric quantile regression

Quantile regression introduced by Koenker and Bassett (1978) produces a comprehensive picture of a response variable on predictors. In this paper, we propose a general semi-parametric model of which part of predictors are presented with a single-index, to model the relationship of conditional quantiles of the response on predictors. Special cases are single-index models, partially linear single-index models and varying coefficient single-index models. We propose the qOPG, a quantile regression version of outer-product gradient estimation method (OPG, Xia et al., 2002) to estimate the single-index. Large-sample properties, simulation results and a real-data analysis are provided to examine the performance of the qOPG.     

Generation of pseudo random numbers and estimation under Cox models with time-dependent covariates

ABSTRACT

We study the method for generating pseudo random numbers under various special cases of the Cox model with time-dependent covariates when the baseline hazard function may not be constant and the random variable may equal infinity with a positive probability. During our simulation studies in computing the partial likelihood estimates, in between 3% and 20% of the time with a moderate sample size, it happens that the partial likelihood estimate of the regression coefficient is ∞ for the data from the Cox model. We propose a semi-parametric estimator as a modification for such a case. We present simulation results on the asymptotic properties of the semi-parametric estimator.     

Semi-parametric Efficient Inference for Heteroscedastic Semivarying-coefficient Models

Semivarying-coefficient models with heteroscedastic errors are frequently used in statistical modeling. When the error is conditional heteroskedastic, Ahmad, et al. (2005 Ahmad, I., Leelahanon, S., Li, Q. (2005). Efficient estimation of a semiparametric partially linear varying coefficient model. Ann. Statist. 33(1):258–283.[Crossref], [Web of Science ®] , [Google Scholar]) proposed a general series method to obtain an efficient estimation. In this article we study the heteroscedastic semi-varying coefficient models with a nonparametric variance function, not only use the semi-parametric efficient normal approximation method to derive a family of semi-parametric efficient estimator, but also use the semi-parametric efficient empirical likelihood method to construct the efficient empirical likelihood confidence regions. The proposed estimators retain the double robustness feature of semi-parametric efficient estimator.     

Asymptotic efficiency of a competing risks model: A two-sample case

A generalized Cox regression model is studied for the covariance analysis of competing risks data subject to independent random censoring. The information of the maximum partial likelihood estimates is compared with that of maximum likelihood estimates assuming a log linear hazard function.The method of generalized variance is used to define the efficiency of estimation between the two models. This is then applied to two-sample problems with two exponentially censoring rates. Numerical results are summarized ane presented graphically.The detailed results indicate that the semi-parametric model wrks well for a higher rate of censoring. A method of generalizing the result to type 1 censoring and the efficiency of estimating the coefficient of the covariate are discussecd. A brief account of using the results to help design experiments is also given.     

Robust confidence regions for the semi-parametric regression model with responses missing at random

In this paper, a regression semi-parametric model is considered where responses are assumed to be missing at random. From the empirical likelihood function defined based on the rank-based estimating equation, robust confidence intervals/regions of the true regression coefficient are derived. Monte Carlo simulation experiments show that the proposed approach provides more accurate confidence intervals/regions compared to its normal approximation counterpart under different model error structure. The approach is also compared with the least squares approach, and its superiority is shown whenever the error distribution in the simulation study is heavy tailed or contaminated. Finally, a real data example is given to illustrate our proposed method.     

Inferences in dynamic logit models in semi-parametric setup for repeated binary data

Binary dynamic fixed and mixed logit models are extensively studied in the literature. These models are developed to examine the effects of certain fixed covariates through a parametric regression function as a part of the models. However, there are situations where one may like to consider more covariates in the model but their direct effect is not of interest. In this paper we propose a generalization of the existing binary dynamic logit (BDL) models to the semi-parametric longitudinal setup to address this issue of additional covariates. The regression function involved in such a semi-parametric BDL model contains (i) a parametric linear regression function in some primary covariates, and (ii) a non-parametric function in certain secondary covariates. We use a simple semi-parametric conditional quasi-likelihood approach for consistent estimation of the non-parametric function, and a semi-parametric likelihood approach for the joint estimation of the main regression and dynamic dependence parameters of the model. The finite sample performance of the estimation approaches is examined through a simulation study. The asymptotic properties of the estimators are also discussed. The proposed model and the estimation approaches are illustrated by reanalysing a longitudinal infectious disease data.     

Asymptotic properties of the estimators of the semi-parametric spatial regression model

Spatial data and non parametric methods arise frequently in studies of different areas and it is a common practice to analyze such data with semi-parametric spatial autoregressive (SPSAR) models. We propose the estimations of SPSAR models based on maximum likelihood estimation (MLE) and kernel estimation. The estimation of spatial regression coefficient ρ was done by optimizing the concentrated log-likelihood function with respect to ρ. Furthermore, under appropriate conditions, we derive the limiting distributions of our estimators for both the parametric and non parametric components in the model.     

Semiparametric Smooth Coefficient Models

In this article, we propose a semiparametric smooth coefficient model as a useful yet flexible specification for studying a general regression relationship with varying coefficients. The article proposes a local least squares method with a kernel weight function to estimate the smooth coefficient function. The consistency of the estimator and its asymptotic normality are established. A simple statistic for testing a parametric model versus the semiparametric smooth coefficient model is proposed. An empirical application of the proposed method is presented with an estimation of the production function of the nonmetal mineral industry in China. The empirical findings show that the intermediate production and management expense has played a vital role and is an unbalanced determinant of the labor and capital elasticities of output in production.