【作者】 陈海燕；

【导师】 杨宝臣；

【作者基本信息】 天津大学 ， 技术经济及管理， 2010， 博士

【摘要】 面板数据模型的检验方法研究是现代计量经济学的重要内容之一,具有显著的理论意义和现实意义。本文主要对面板异方差和序列相关检验、面板单位根检验和面板协整检验等研究内容进行了系统的研究,完善和发展了面板数据模型的相关检验方法。主要创新工作有:(1)提出了适用于单向固定效应模型和随机效应模型的异方差确定性检验方法。在面板回归模型中,异方差因误差项具有个体和时间双重效应而具有多种情形,给实证研究带来诸多不便。本文在一个检验框架内对单向面板回归模型中异方差的七种类型进行了研究,将误差项中的个体效应和时间效应进行了分离,给出了异方差类型的确定性检验方法和步骤,有效的提高了检验效率。应用我国城镇居民总消费、居住消费和收入的数据进行了实证分析,证实了异方差类型确定性检验方法的实用性。(2)通过Monte Carlo模拟研究发现,传统的面板单位根检验对带均值结构变化的面板数据只有在突变前后样本数相差很大,或者均值变化不明显,或者N或T极大时,才不会导致单位根检验结果失效;而传统的面板单位根检验对趋势变化的面板数据产生伪检验的可能性非常大。说明结构变化的忽视将导致面板单位根检验结果严重失真。(3)提出了LSTR-IPS面板单位根检验方法。为了降低结构变化对面板单位根检验的影响,利用平滑修正技术对IPS单位根检验进行修正,通过模拟研究说明LSTR-IPS比传统的面板单位根检验方法具有更高的功效,并且当样本量越大时,LSTR-IPS检验的功效越高,特别是随着时间T的增大,检验功效呈上升趋势。应用我国经济增长数据进行平稳性检验,表明经济增长为带结构变化的趋势平稳过程而非一阶单整过程,说明LSTR-IPS检验能更准确地判断面板数据的平稳性。(4)考虑到结构变化具有渐近性和逐步性的特征,利用平滑技术对Westerlund和Edgerton(2008)中的面板协整检验进行了修正,提出了基于LSTR模型的考虑截面相关的面板变结构协整检验,给出了基于LM检验原理的两个检验统计量,论证了不受结构变化点和截面相关影响的渐近正态分布,并通过模拟研究说明了利用平滑转换函数修正的面板协整检验方法具有更高的检验功效。更多还原

【Abstract】 Testing in panel data models becomes one of the most important contents of modern econometric theory and method, which has remarkable significance in academic and application. This paper is devoted to making a systematic research on theory and method of panel heteroskedasticity and serial dependence tests, panel unit root tests and panel cointegration tests, and to perfect and expand the tests in panel data models.In summary, the innovation research conclusion is as follows:(1) This paper proposes the certainty tests of heteroskedasticity for one-way fix effect and random effect panel model. The difficulties of empirical research come from the diversity of heteroscedasticity in panel regression model. This paper investigates the seven types of heteroskedasticity for one-way panel regression model in a test framework. The error term is decomposed into the individual effects and time effects, and then the methods and procedures of the certainty tests of heteroskedasticity are given, simultaneously improve the testing efficiency. An empirical analysis is conducted to confirm the usefulness of the heteroskedasticity certainty test, with total consumption, housing consumption and income data of urban residents in China.(2) Spurious unit root tests of panel data with structural change is investigated in this paper through Monte Carlo simulation. The result shows that unit root tests in panel data with expectation shift are effective only when the sample before and after the turning point varies greatly, or expectation shift is not obvious, or the sample N orT is relatively large; and unit root tests in panel data with trend shift are uneffective and will lead to spurious result in most cases. They show that neglect of structural breaks will lead to test failure.(3) This paper proposes a LSTR-IPS panel data unit root test with structural change, by modifying the traditional IPS unit test with nonlinear smooth transition function. The simulation results show that LSTR-IPS test has much more test power than the traditional unit root tests, and furthermore, the bigger sample size contributes a large increase of the test power for the LSTR-IPS test, especially with the increase of time horizon T, the test power of LSTR-IPS test increase obviously. Finally, as an example, we conduct unit root test to the regional GDP growth using LSTR-IPS test, with structural change rather than a unit root process which is previously being concluded, and thus, it is indicated that the LSTR-IPS test has more accuracy and power for unit root test in panel data.(4) Taking into account the progressive nature and smooth asymptotic characteristics of structural change, this paper proposes a cointegration test using LSTR model. The tests are enough to allow for cross-dependence and structural break in both intercept and slope of the cointegrated regression, which may be located at different dates different units. Two statistics by using the LM based tests are developed, and their limiting distributions are derived, and are found to be normal and free of structural break point and cross-dependence. A simulation study shows that these tests have the better small-sample properties.更多还原