平滑转移空间随机前沿模型的贝叶斯估计

经济数据常存在空间相关性,忽略空间相关性会引发内生性问题,导致相应估计量有偏且不一致。空间随机前沿模型在随机前沿模型的基础上考虑了生产单元的空间相关性,更利于效率测算。然而现有空间随机前沿模型的生产函数形式单一,适用性较差,实证分析存在局限性。文章在空间随机前沿模型中引入平滑转移效应,构建了平滑转移空间随机前沿模型,该模型同时考虑了空间相关性和个体异质性,适用性较佳。为丰富估计方法,同时采用极大似然方法和贝叶斯方法估计模型,其中极大似然估计的核心在于推导对数似然函数、对数似然函数的最优化以及使用JLMS法估计技术效率,贝叶斯估计的核心在于推导未知参数的后验分布及执行MCMC抽样。数值模拟结果显示：（1）极大似然估计和贝叶斯估计的估计精度均较高,其中贝叶斯估计的估计精度略高于极大似然估计;增加样本容量,贝叶斯估计和极大似然估计的估计精度更高。（2）若忽略空间效应或者平滑转移效应,则估计精度较低。     

面板数据随机前沿分析的研究综述

近年来,面板数据随机前沿分析（SFA）越来越多地被用于测算各类决策单位的效率,取得了很多成果,但是国内外实证研究文献也存在过度依赖几种假设严格的模型和不注重模型局限性的问题。本文在统一的计量框架下,对面板SFA模型的发展研究进行了系统的梳理总结。本文将相关模型分为两大类：效率不随时间变化的模型和效率可随时间变化的模型,每一类又根据是否对效率项的分布做出假设分为：有分布假设的模型和无分布假设的模型。在明确和比较不同模型的假设、估计过程和局限性的基础上,我们对未来面板SFA模型的应用提出了建议。     

空间滞后分位数回归模型的工具变量估计及参数检验

本文提出一种遵循空间数据分布特征的空间分位数回归模型,并着重探讨该模型的估计方法和参数检验问题。本文构建了上述模型的一个工具变量估计法,通过数理证明建立了估计量的大样本理论,并基于估计量的渐近分布构造了模型的参数检验方法。本文还通过数值模拟方法和应用实例考察估计方法和参数检验方法的实际应用效果,数值模拟结果显示,估计方法和参数检验方法在有限样本条件下均可以达到较高的精确度和稳定性。在应用实例中,本文利用所构建的理论方法重新检验我国“资源诅咒”效应的存在性,实证结果体现了理论方法的应用价值。     

具有组群异方差结构的面板数据模型及其应用研究

面板数据由不同个体的时间序列数据汇聚而成。已有大量研究表明面板数据个体之间存在组群结构,并且普遍存在模型的异方差现象。本文借鉴组群异质性的研究成果,构建模型误差项组群结构的面板数据模型,基于模型假定条件,提出惩罚伪最大似然函数估计法(PQMLE),该方法能够同时进行结构识别和参数估计;证明了估计量具有Oracle渐近性质;蒙特卡洛模拟验证了该方法有效的样本性质;进一步应用该方法对我国股市进行Fama-French三因子模型的实证分析,验证了理论模型的应用效果。     

异质性空间随机前沿模型的参数估计及应用

经典的随机前沿模型忽略了决策单元之间的空间关联性，无法准确估计效率影响因素相关参数，限制了其适用范围。本文在空间自回归随机前沿模型的基础上，引入效率影响因素，构建出一个异质性空间随机前沿模型，基于极大似然估计法给出模型参数的单步估计策略，提出决策单元技术效率的最优预测量。理论分析证明，模型参数在一定的假设条件下具备一致性；模拟实验表明，参数估计量和技术效率预测量较之经典模型具有更高的估计精度，且会随着样本量的扩大而逐渐提升。本文使用所提出理论方法讨论了我国城市数字普惠金融发展与技术效率水平之间的相关关系，发现两者之间存在显著的正相关关系，同时也印证了模型设定和估计方法的可靠性。     

真实固定效应空间随机前沿模型的贝叶斯估计

忽略个体效应和空间效应会严重干扰效率测算,其中忽略个体效应使得技术无效率项发生偏移,忽略空间相关性导致估计量有偏且不一致。本文基于真实固定效应随机前沿模型（引入了个体效应）,引入因变量和双边误差项的空间滞后项,构建了适用性更佳的真实固定效应空间随机前沿模型。对模型进行组内变化以消除额外参数,使用贝叶斯方法（需推导未知参数的后验分布并执行MCMC抽样）估计参数和技术效率。该方法真正克服了额外参数问题,比同类方法直观、简便。数值模拟结果表明,本文方法对参数、个体截距项及技术无效率项的估计精度均较高,且增加样本容量,估计精度变优。     

医疗保险定点医院损失分布估计

本文应用了风险模型的损失分布及其估计方法,在分析社会医疗保险医疗赔付模式下,根据定点医院相关数据对风险模型的损失分布进行了实证分析和估计。并采用蒙特卡罗方法进行了数据仿真,表明估计结果的有效性。     

基于CIR和Vasicek模型的利率风险计量及实证——以我国国债市场为例

本文以我国国债市场为例,利用四种期限类型(7年期、8年期、10年期和20年期)的国债收益率样本数据对CIR模型和Vasicek模型进行实证分析得出,CIR模型和Vasicek模型较适宜于中国当前的金融市场实际。本文使用Nowman(1997)提出的最大似然估计法(MLE)对上述利率动态模型进行估计,以解决实证研究中广义矩方法(GMM)可能存在诸如效率不高的问题;并基于CIR模型和Vasicek模型构建了利率期限结构调整久期模型。     

DEA方法在潜在GDP估算中的应用

文章总结了前人对潜在国内生产总值(GDP)测算方法研究的成果与不足,对潜在GDP的数据包络分析估算方法进行了探讨。文章深入分析了BCC模型的经济意义,应用数据包络方法的潜在GDP测算法,估计了中国1998～2007年间的宏观经济投资效率和潜在GDP。应用数据包络方法估算潜在GDP,有利于克服传统潜在GDP测算方法的缺陷与不足,从而使潜在GDP的估算更加科学与可靠。     

一种新的空间权重矩阵选择方法

空间权重矩阵选择问题一直是空间计量经济学中的一个难题,权重矩阵的选择正确与否关系到模型的最终估计结果。本文在空间滞后模型框架下,把空间权重矩阵选择问题转化为变量选择问题,然后利用CWB方法进行变量选择。中国城市服务业集聚机理实证研究显示,利用本文所提出的方法所选取的空间权重矩阵较为合理,进而可以减少因为空间权重矩阵误设问题而引起的模型估计偏误。在大样本情形下,该方法可以非常有效地降低计算成本。     

空间ZISF的估计及蒙特卡罗模拟

在随机前沿模型中引入空间效应和技术无效率项的非连续性并构建了空间零无效率随机前沿模型,使用极大似然估计和JLMS方法得出参数和技术效率的估计。蒙特卡罗模拟表明:(1)逆似然比检验能以较高的准确率识别真实模型;(2)本方法在参数估计和技术效率的估计两方面均表现较好;(3)若真实模型为空间零无效率随机前沿模型但误用了空间随机前沿模型,参数估计和技术效率的估计两方面均表现较差。空间零无效率随机前沿模型有其存在的必要性。     

双重滞后随机前沿模型技术效率的估计

首次在随机前沿模型中同时引入因变量间(或双边误差间)和技术效率间的空间相关性并构造了双重滞后随机前沿模型,使用极大似然估计方法和JLMS方法得出参数和技术效率的估计。蒙特卡罗模拟表明:忽略技术效率的空间相关性,参数估计和技术效率的估计均表现欠佳。本研究能以较高的精度估计参数和技术效率。随着样本容量的增加,估计效果更优。     

Efficiency Measure from Dynamic Stochastic Production Frontier: Application to Tunisian Textile, Clothing, and Leather Industries

This paper adresses the measurement of technical efficiency of textile, clothing, and leather (TCL) industries in Tunisia through a panel data estimation of a dynamic translog production frontier. It provides a perspective on productivity and efficiency that should be instructive to a developing economy which will face substantial competitive pressure along the gradual economic liberalisation process. The importance of TCL industries in Tunisian manufacturing sector is a reason for obtaining more knowledge of productivity and efficiency for this key industry. Dynamic is introduced to reflect the production consequences of the adjustment costs, which are associated with changes in factor inputs. Estimation of a dynamic error components model is considered using the system generalized method of moments (GMM) estimator suggested by Arellano and Bover (1995), Another look at the instrumental-variable estimation of error-components models, J. Econometrics68:29-51) and Blundell and Bond (Blundell, R., Bond, S. (1998a), Initial conditions and moment restrictions in dynamic panel data models. J. Econometrics87:115-143; Blundell, R., Bond, S. (1998b), GMM estimation with persistent panel data: an application to production functions, Paper presented at the Eighth International Conference on Panel Data, Goteborg University). Our study evaluates the sensitivity of the results, particularly of the efficiency measures, to different specifications. Firm-specific time-invariant technical efficiency is obtained using the Schmidt and Sickles (Schmidt, P., Sickles, R. C. (1984). Production frontiers and panel data. J. Bus. Econ. Stat.2:367-374) approach after estimating the dynamic frontier. We stress the importance of allowing for lags in adjustment of output to inputs and of controlling for time-invariant variables when estimating firm-specific efficiency. The results suggest that the system GMM estimation of the dynamic specification produces the most accurate parameter estimates and technical efficiency measure. Mean efficiency scores is of 68%. Policy implications of the results are outlined.     

Estimation of long-run inefficiency levels: a dynamic frontier approach

Cornwell, Schmidt, and Sickles (1990) and Kumbhakar (1990), among others, developed stochasticfrontier production models which allow firm specific inefficiency levels to change over time. These studies assumed arbitrary restrictions on the short-run dynamics of efficiency levels which have little theoretical justification. Further, the models are inappropriate for estimation of long-run efficiencies. We consider estimation of an alternative frontier model in which firmspecific technical inefficiency levels are autoregressive. This model is particularly useful to examine a potential dynamic link between technical innovations and production inefficiency levels. We apply our methodology to a panel of US airlines.     

Estimation of long-run inefficiency levels: a dynamic frontier approach

Cornwell, Schmidt, and Sickles (1990) and Kumbhakar (1990), among others, developed stochasticfrontier production models which allow firm specific inefficiency levels to change over time. These studies assumed arbitrary restrictions on the short-run dynamics of efficiency levels which have little theoretical justification. Further, the models are inappropriate for estimation of long-run efficiencies. We consider estimation of an alternative frontier model in which firmspecific technical inefficiency levels are autoregressive. This model is particularly useful to examine a potential dynamic link between technical innovations and production inefficiency levels. We apply our methodology to a panel of US airlines.     

A copula regression model for estimating firm efficiency in the insurance industry

This article considers the estimation of insurers’ cost-efficiency in a longitudinal context. The current practice ignores the tails of the cost distribution, where the most and least efficient insurers belong to. To address this issue, we propose a copula regression model to estimate insurers’ cost frontier. Both time-invariant and time-varying efficiency are adapted to this framework and various temporal patterns are considered. In our method, flexible distributions are allowed for the marginals, and the subject heterogeneity is accommodated through an association matrix. Specifically, when fitting to the insurance data, we perform a GB2 regression on insurers total cost and employ a t-copula to capture their intertemporal dependencies. In doing so, we provide a nonlinear formulation of the stochastic panel frontier and the parameters are easily estimated by likelihood-based method. Based on a translog cost function, the X-efficiency is estimated for US property-casualty insurers. An economic analysis provides evidences of economies of scale and the consistency between the cost-efficiency and other performance measures.     

空间滞后面板平滑转换模型的估计及数值模拟

将空间滞后项引入面板平滑转换模型,构建了空间滞后面板平滑转换模型,通过综合应用拟极大似然法和非线性最小二乘法,构造了该模型的参数估计方法,并通过蒙特卡洛数值模拟探讨了参数估计方法的小样本性质;数值模拟结果显示,提出的估计方法在小样本条件下表现良好,参数估计值随着样本容量的增大而收敛到参数的真值。     

随机效应空间滞后单指数面板模型

对随机效应空间滞后单指数面板模型,本文构建了该模型的截面极大似然估计方法,从理论证明和数值模拟两方面分别考察了其估计量的大样本性质和小样本表现。研究结果表明:(1)在大样本条件下,估计量均具有一致性,并且参数估计量具有渐近正态性。(2)在小样本条件下,各估计量依然具有良好的表现,其精度随着样本容量的增加而提高;空间权重矩阵结构的复杂性对空间相关系数的估计量影响较大,但对其他估计量的影响较小。     

Semiparametric Smooth Coefficient Stochastic Frontier Model With Panel Data

ABSTRACT

We investigate the semiparametric smooth coefficient stochastic frontier model for panel data in which the distribution of the composite error term is assumed to be of known form but depends on some environmental variables. We propose multi-step estimators for the smooth coefficient functions as well as the parameters of the distribution of the composite error term and obtain their asymptotic properties. The Monte Carlo study demonstrates that the proposed estimators perform well in finite samples. We also consider an application and perform model specification test, construct confidence intervals, and estimate efficiency scores that depend on some environmental variables. The application uses a panel data on 451 large U.S. firms to explore the effects of computerization on productivity. Results show that two popular parametric models used in the stochastic frontier literature are likely to be misspecified. Compared with the parametric estimates, our semiparametric model shows a positive and larger overall effect of computer capital on the productivity. The efficiency levels, however, were not much different among the models. Supplementary materials for this article are available online.