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重印：随机前沿生产函数模型的设定与估计AI 解读

Previous studies of the so-called frontier production function have not utilized an adequate characterization of the disturbance term for such a model. In this paper we provide an appropriate specification, by defining the disturbance term as the sum of symmetric normal and (negative) half-normal random variables. Various aspects of maximum-likelihood estimation for the coefficients of a production function with an additive disturbance term of this sort are then considered.

多层模型估计的计量经济学方法AI 解读

In this paper we consider “multidimensional” or “hierarchical” or “multilevel” models that are popular in the educational and economics literatures. Instead of two levels (individuals over time in the standard panel data model), we now have multiple levels (e.g. students in classrooms in schools in districts). We apply standard methods of analysis for econometric panel data to multilevel models. Specifically, we generalize the results of Hausman and Taylor and the subsequent…

随机前沿模型中的内生环境变量AI 解读

This paper considers a stochastic frontier model that contains environmental variables that affect the level of inefficiency but not the frontier. The model contains statistical noise, potentially endogenous regressors, and technical inefficiency that follows the scaling property, in the sense that it is the product of a basic (half-normal) inefficiency term and a parametric function of the environmental variables. The environmental variables may be endogenous because they…

随机前沿模型中的内生性AI 解读

Stochastic frontier models are typically estimated by maximum likelihood (MLE) or corrected ordinary least squares. The consistency of either estimator depends on exogeneity of the explanatory variables (inputs, in the production frontier setting). We will investigate the case that one or more of the inputs is endogenous, in the simultaneous equation sense of endogeneity. That is, we worry that there is correlation between the inputs and statistical noise or inefficiency.

固定效应随机前沿模型的一致估计AI 解读

In this paper we consider a fixed-effects stochastic frontier model. That is, we have panel data, fixed individual (firm) effects, and the usual stochastic frontier analysis (SFA) composed error.

具有多个时变个体效应的面板数据模型AI 解读

This paper considers a panel data model with time-varying individual effects. The data are assumed to contain a large number of cross-sectional units repeatedly observed over a fixed number of time periods. The model has a feature of the fixed-effects model in that the effects are assumed to be correlated with the regressors. The unobservable individual effects are assumed to have a factor structure. For consistent estimation of the model, it is important to estimate the…

面板环境中的似然估计：Copula 的稳健性、冗余性与有效性AI 解读

This paper considers the estimation of likelihood-based models in a panel setting. That is, we have panel data, and for each time period separately we have a correctly specified model that could be estimated by MLE. We want to allow non-independence over time. This paper shows how to improve on the QMLE. It then considers MLE based on joint distributions constructed using copulas. It discusses the efficiency gain from using the true copula, and shows that knowledge of the…

随机前沿模型中估计技术效率的分布AI 解读

We consider a stochastic frontier model with error [epsilon]=v-u, where v is normal and u is half normal. We derive the distribution of the usual estimate of u,E(u[epsilon]). We show that as the variance of v approaches zero, E(u[epsilon])-u converges to zero, while as the variance of v approaches infinity, E(u[epsilon]) converges to E(u). We graph the density of E(u[epsilon]) for intermediate cases. To show that E(u[epsilon]) is a shrinkage of u towards its mean, we derive…

个体效应随时间参数化变化的面板数据模型估计AI 解读

This paper is an extension of Ahn, Lee and Schmidt (2001) to allow a parametric function for time-varying coefficients on the individual effects. It is shown that the main results of Ahn, Lee and Schmidt (2001) hold for our model too. Least squares is consistent, given white noise errors, but less efficient than a GMM estimator. An application of the GMM estimators to the measurement of cost efficiency of Spanish banks is also included. The empirical study shows the…

与最优者的多重比较及其经济应用AI 解读

In this paper we discuss a statistical method called multiple comparisons with the best, or MCB. This paper is meant to introduce MCB to economists. We discuss possible uses of MCB in economics. The application that we treat in most detail is the construction of confidence intervals for inefficiency measures from stochastic frontier models with panel data. We also consider an application to the analysis of labour market wage gaps. Copyright � 2000 John Wiley & Sons, Ltd.

动态面板数据模型的有效率估计AI 解读

In this paper we consider a dynamic model for panel data. We show that, under standard assumptions, there are more moment conditions than are currently exploited in the literature. Some of these are linear, but others are quadratic, so that nonlinear GMM is required. We also show that exogenous regressors generate a larger number of relevant moment conditions in a dynamic model than they would in a static model.

工具变量并非严格外生时含序列相关的面板模型估计：评论AI 解读

Peter Schmidt, Seung C. Ahn, Donald Wyhowski, [On the Estimation of Panel-Data Models with Serial Correlation When Instruments Are Not Strictly Exogenous]: Comment, Journal of Business & Economic Statistics, Vol. 10, No. 1 (Jan., 1992), pp. 10-14

利用面板数据进行有效估计AI 解读

IN AN IMPORTANT RECENT PAPER, Hausman and Taylor (1981)-hereafter HT-considered the instrumental-variable estimation of a regression model using panel data, when the individual effects may be correlated with a subset of the explanatory variables. They provided a simple consistent estimator and an efficient estimator. More recently, Amemiya and MaCurdy (1986)-hereafter AM-have suggested an alternative estimator which is more efficient than the HT estimator, under certain…

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