Stochastic Frontier Models: Theory and Application
Date Issued
2014
Date
2014
Author(s)
Su, Hsin-Wei
Abstract
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.
Subjects
隨機邊界模型
模型設定檢定
動態追蹤模型
兩階段估計
動差法
準最大概似估計
Type
thesis
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