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  • 學位論文

隨機邊界模型:理論與應用

Stochastic Frontier Models: Theory and Application

指導教授 : 王泓仁

並列摘要


The theme of this thesis seeks to find modern testing techniques and estimation methods to support and extend the application of the stochastic frontier models. With a long time development, stochastic frontier (SF, hereafter) models have various homogenous and heterogeneous model specifications, especially on the distribution of the inefficient term. Although many tests in the literature of SF models can help us choose the suitable model specification, these tests can not help us know if we need to use a heterogeneous specification or what kind of heterogeneous specification we should use in SF analysis. Hence, to find a test that can test most kinds of SF models is the first aim of this thesis. On the other hand, with the extension to other econometric fields, the SF analysis requires to use the panel data more frequently. Dynamic panel SF models are models which contains the features of both dynamic panel models and SF models and have the value in SF analysis when using panel data. However, this kind of models are more difficult to estimate than either dynamic panel models or SF models. To seek a way to consistently estimate most kinds of dynamic panel SF models is the second aim of this thesis. In this thesis, three chapters are generated to discuss the aforementioned testing and estimating issues in SF models: 1. Evaluating Stochastic Frontier Models by the Simulated Integrated Conditional Moment Test The problem of testing the distribution of the composite error or the functional form of the frontier function in the SF models has become increasingly important in recent years. However, the tests mentioned in the literature of SF analysis are not able to jointly test the misspecification of different aspects of SF models, especially the distribution of the composite error and the functional form of the frontier function. The lack of appropriate tests may lead to incorrect model specifications for empirical analysis. This paper applies the SICM test of Bierens and Wang (2012) to SF models.The SICM test is a consistent test with p n non-trivial local power, and it can detect comprehensively the misspecification of many aspects of the model. This paper also demonstrates the validity and advantages of this test in practical applications using a i Monte Carlo simulation. 2. Moment Estimators for Dynamic Panel Stochastic Frontier Models with Fixed-Effect SF models have widely applied in more and more econometric fields, but this extension brings new questions and challenges. When analyzing the panel data with some dynamic property, the researchers now may encounter a dynamic panel SF model which means there is the incidental parameter problem caused by an unobserved individual variable and the lagged terms of the dependent variable in the production function of the SF models. This chapter tries to find an estimation strategy which may consistently estimate the parameters of the dynamic panel SF model. Referring to Chen and Wang (2014), the estimation strategy contains two step. The first step applies the dynamic panel generalized method of moments (GMM, hereafter) method to estimate the parameters of the production function. In the second step, we use the method of moments to obtain the moment estimators of the distribution parameters of the composite error. The simulation results demonstrate that these estimators may be consistent when the numbers of individual go to infinity. 3. Quasi-Maximum Likelihood Estimation for Heteroscedastic Dynamic Panel Stochastic Frontier Models SF models with heteroscedastic composite errors can analyze the factors that influence the inefficient term and have become highly popular recently. This chapter succeeds the work of the second to find a consistent estimation method for dynamic SF model with heteroscedastic composite errors. Two-step approach is still be adopted but with some changes for estimating heteroscedastic dynamic panel SF models. In the first step, the dynamic panel GMM technique is still be applied to estimate the parameters of production function, but in the second step, the quasi-maximum likelihood (QML, hereafter) estimation is conducted to obtain the distribution parameter estimators of the composite error. The identification of QML estimation confirms the consistency of this method. The simulation results also illustrate that the estimators of heteroscedastic dynamic panel SF models are consistent when one uses this two-step approach.

參考文獻


Wang, H.-J. and Schmidt, P. (2002). “One-Step and Two-Step Estimation of the Effects of
Ahn, S.C., Good, D.H. and Sickles, R.C. (2000). “Estimation of Long-Run Inefficiency Levels:
Anderson, T.W. and Hsiao, C. (1982). “Formulation and Estimation of Dynamic Models Using
Panel Data,” Journal of Econometrics 18, pp. 47-82.
Andrews, D.W. (1997). “A Conditional Kolmogorov Test,” Econometrica 65, pp. 1097-1128.

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