动态面板数据模型估计及其内生结构突变检验理论与应用
|
摘要
从现存的文献可以看出，面板数据模型(Panel Data model)自20世纪60年代提出以来，已成为现代计量经济学的一个重要分支，而其中的动态面板数据模型(Dynamic Panel Data Model)，正是这一分支的前沿和研究焦点。所谓动态面板数据模型，是指通过在静态面板数据模型中引入滞后被解释变量以反映动态滞后效应的模型。这种模型的特殊性在于被解释变量的动态滞后项与随机误差组成部分中的个体效应相关，从而造成估计的内生性。正是这种内生性导致模型的估计和检验的困难。进一步，如何检验动态面板数据模型的内生结构突变，无疑是这一方向最为困难的问题。本文着重研究动态面板数据模型的估计与内生结构突变的检验问题，全文的研究工作和研究结论可以概括为以下几个方面：
(1)深入系统地解析了动态面板数据模型的估计偏误、主要的GMM估计方法与假设条件以及弱工具变量问题等，在此基础上，通过比较分析揭示了主要的GMM估计量的统计性质和适用条件，有偏性或弱有效的来源以及如何校正。我们的主要研究结果为：动态面板数据模型的各种估计方法，对于不同的数据类型，有着不同的适用性。系统GMM估计在大多数情况下，估计量具有最小的偏误，也最有效，但如果个体效应与异质性冲击的方差比非常小，特别是非常大时，估计量的偏误程度可能大于一阶差分的GMM估计结果；一阶差分GMM估计量的偏误程度随自回归系数趋近于1而显著增大，但其对方差比变化的敏感性较弱；水平方程的GMM估计对方差比变化的敏感性较大，在自回归系数较大时，估计量的偏误程度要小于一阶差分GMM估计；固定效应有偏估计量基础之上的偏误纠正方法，特别是Hansen(2001)的纠正方法，当模型中没有其他前定解释变量时，估计量的偏误程度最小，当模型中存在其他前定解释变量时，其估计量的有限样本性质相对较差。
(2)针对我国省际面板数据所能使用的横截面个数最大为31，而现有文献中，动态面板数据模型GMM估计量偏误的仿真实验研究所设计的截面数大都远远大于此个数，从而使我国学者在应用省际数据进行动态经济分析时，对估计量偏误程度不能较好度量的问题，考虑各种影响估计偏误的因素，本文设计和实现了相应的仿真实验，揭示了横截面个数N为30的情况下，三种代表性的动态面板数据模型GMM估计方法自回归系数估计量的偏误程度。基于仿真实验结果，在以我国省际面板数据进行动态模型分析时，本文建议：时间维度T的取值在10—15之间为宜；对于较小的自回归系数和较大的个体效应与异质性冲击的方差比，使用一阶差分GMM估计；个体效应与异质性冲击的方差比接近于1时，使用系统GMM估计；个体效应与异质性冲击的方差比较大时，自回归系数在接近于0．5时，使用向前正交离差GMM估计，在接近于1时，使用系统GMM估计；除非自回归系数和个体效应与异质性冲击的方差比都非常小，三种估计方法都不要使用所有潜在的工具变量，建议所选取的工具变量不要超过滞后4期。
(3)深入系统地阐述了动态面板数据模型结构突变的检验及估计问题，通过结构突变点前、后以及突变点处矩函数的变化，说明了模型如果确实存在着结构突变，忽略它，GMM方法估计量不但是有偏的而且是非一致的；重点介绍了Wachter(2004)的动态面板数据模型内生结构突变检验的理论框架，识别方法及其检验统计量的结构，讨论了其检验统计量的有限样本性质；使用Wachter(2004)的动态面板数据模型的内生结构突变识别方法，对中国通货膨胀惯性模型和居民消费函数模型进行未知结构突变检验，根据检验结果，使用系统GMM估计方法对模型参数进行估计，并同未考虑结构突变的估计结果进行了比较。
基于上述，全文的创新和意义为：(1)不同于现有文献，本文从数据生成过程对模型估计量的偏误性及有效性影响程度的角度出发，揭示了各种主要的动态面板数据模型估计方法的适用条件和估计量的统计性质；(2)本文设计并实现的仿真实验所使用的截面个数，为现有文献中对动态面板数据模型估计偏误仿真研究中最小的，其结果对于如何应用我国省际动态面板数据研究现实经济问题，具有针对性的意义；(3)系统地阐述了动态面板数据模型结构突变的检验及估计这一计量经济学的前沿问题，首次向国内引入允许模型的斜率系数及个体效应在未知时刻存在着结构突变，并且不约束后者的突变具有同质性的动态面板数据模型内生结构突变检验方法；(4)首次使用Wachter检验方法，对中国通货膨胀惯性和居民消费函数动态面板数据模型进行未知结构突变检验，结果显示两个模型都存在着显著的结构性变化，说明了在研究我国经济问题，进行计量分析时，对模型进行结构突变的检验，防止模型错误设定有着重要的意义。
The existing literatures show that panel data model has been an important branch of modern econometrics ever since proposed in 1960s,of which the dynamic panel data model is the forefront and research focus of this branch.The so-called Dynamic Panel Data Model, is the panel data model which reflects the dynamic hysteresis effect by introducing lagged response variables to the static model. The specificity of this model lies in that the lagged item of dependent variable is related to the individual effect of random error components, resulting in the estimator endogenous. Further, how to test the endogenous structural breaks of dynamic panel data model is undoubtedly the most difficult problems in this direction. This article focuses on the dynamic panel data model estimate and the endogenous structural breaks testing, and its research contents and conclusions can be summarized as following:
Firstly,this paper formulates thoroughly and systematically the estimator bias, the main estimate method and assumptions of GMM, as well as the weak instrumental variable and so on. On this basis, it reveals the statistical nature and application condition, the source of bias or weak effect of the major estimators of GMM,and how to correct. The major findings are that the various estimate methods of dynamic panel data model applies different data generation process; the estimator bias resulting from the system GMM is the smallest and most effective in most cases, but if the variance ratio of individual effects and idiosyncratic shocks is very small,especially when very large,the bias degree of estimator may be greater than the estimate results from differenced GMM; the bias degree of estimator from the differenced GMM increases significantly with the auto-regression coefficients close to 1, but its sensitivity to the changes of variance ratio is weaker;the estimator of GMM of the level equation shows larger sensitivity to the changes of variance ratio, the bias degree of estimator is smaller than the estimator of differenced GMM when the auto-regression coefficient is larger; The bias correction method based on the fixed effect bias estimator, especially the approach proposed by Hansen (2001) has the smallest degree of bias when there are no other predetermined variables in model.otherwise,the nature of limited samples if relatively poor.
Secondly, the basic feature of China's provincial-level panel data is that the largest useful number of cross-sectional is 31 .but in existing literatures, the simulation study of dynamic panel data model estimation bias under the framework of the GMM designs much greater cross-section numbers than the number of China's provinces,so that Chinese scholars have not a better measure of estimate bias when analysing China's provincial-level dynamic panel data. Considering all the factors effecting the estimated bias,the author designs and realizes the corresponding simulation study, and finds the bias degree of autoregression coefficient estimator with three representative dynamic panel data model on the condition that the cross-section number is 30. Based on simulation results, when doing dynamic model analysis in China's provincial-level panel data ,this paper recommends as following: the appropriate values of time dimensionis 10 to 15; for smaller auto-regression coefficient and larger variance ratio of individual effects and idiosyncratic shocks, applies differenced GMM;the variance ratio of individual effects and idiosyncratic shocks close to 1, using the system GMM;when the variance ratio of individual effects and idiosyncratic shocks is large,if the auto- regression coefficient is close to 0.5,the forward orthogonal deviation GMM applies;if close to 1, the system GMM applies;Unless the auto-regressive coefficients and the variance ratio of individual effects and idiosyncratic shocks are all very small, all three estimation methods do not use the potential instrumental variables at all.and it is recommended to select instrumental variables with lag periods less than 4.
Thirdly, this paper formulates thoroughly and systematically the structure breaks testing and estimation of dynamic panel data model, if there is indeed structural breaks, ignoring it,the estimator of GMM is not only biased but also non-consistent through the change of the moment function before and after, as well as breaks point. this paper Focuses on the endogenous structural breaks testing's theoretical framework, identification methods and the structure of test statistic of Wachter (2004)'s dynamic panel data model; And do unknown structural breaks test to the model of inflation inertia in Wachter (2004)'s endogenous structural breaks test method of dynamic panel data model. in accordance with the test results, this paper estimates the model parameters in the system GMM and compares to the estimator without considering structural breaks.
Based on the above, this paper's innovation and meaning lie in: firstly,from the influence of data generation process to estimator bias and validity, this article reveals the statistical nature and application condition of major dynamic panel data model estimator,and this is different from the existing literature;secondly,the cross-section number in the simulation study designed and realized is the least in the existing literature on dynamic panel data model's estimator bias simulation study.And the result shows how to study the economic problems in China's provincial-level panel data;thirdly,this paper firstly introduces the endogenous structural breaks test method of dynamic panel data model method which allowing the slope coefficient and the individual effects of unknown time to be structural breaks ,and the later to be of the endogenous;forthly,do unknown structural breaks test to the model of inflation inertia and inhabitants consumption function in Wachter(2004) test method firstly. The result is that there are all significant structural changes in the two models. So it is much of significance to do structural breaks test to prevent the wrong model-setup in the study of China's economic problems with econometrics analysis.
(1)深入系统地解析了动态面板数据模型的估计偏误、主要的GMM估计方法与假设条件以及弱工具变量问题等，在此基础上，通过比较分析揭示了主要的GMM估计量的统计性质和适用条件，有偏性或弱有效的来源以及如何校正。我们的主要研究结果为：动态面板数据模型的各种估计方法，对于不同的数据类型，有着不同的适用性。系统GMM估计在大多数情况下，估计量具有最小的偏误，也最有效，但如果个体效应与异质性冲击的方差比非常小，特别是非常大时，估计量的偏误程度可能大于一阶差分的GMM估计结果；一阶差分GMM估计量的偏误程度随自回归系数趋近于1而显著增大，但其对方差比变化的敏感性较弱；水平方程的GMM估计对方差比变化的敏感性较大，在自回归系数较大时，估计量的偏误程度要小于一阶差分GMM估计；固定效应有偏估计量基础之上的偏误纠正方法，特别是Hansen(2001)的纠正方法，当模型中没有其他前定解释变量时，估计量的偏误程度最小，当模型中存在其他前定解释变量时，其估计量的有限样本性质相对较差。
(2)针对我国省际面板数据所能使用的横截面个数最大为31，而现有文献中，动态面板数据模型GMM估计量偏误的仿真实验研究所设计的截面数大都远远大于此个数，从而使我国学者在应用省际数据进行动态经济分析时，对估计量偏误程度不能较好度量的问题，考虑各种影响估计偏误的因素，本文设计和实现了相应的仿真实验，揭示了横截面个数N为30的情况下，三种代表性的动态面板数据模型GMM估计方法自回归系数估计量的偏误程度。基于仿真实验结果，在以我国省际面板数据进行动态模型分析时，本文建议：时间维度T的取值在10—15之间为宜；对于较小的自回归系数和较大的个体效应与异质性冲击的方差比，使用一阶差分GMM估计；个体效应与异质性冲击的方差比接近于1时，使用系统GMM估计；个体效应与异质性冲击的方差比较大时，自回归系数在接近于0．5时，使用向前正交离差GMM估计，在接近于1时，使用系统GMM估计；除非自回归系数和个体效应与异质性冲击的方差比都非常小，三种估计方法都不要使用所有潜在的工具变量，建议所选取的工具变量不要超过滞后4期。
(3)深入系统地阐述了动态面板数据模型结构突变的检验及估计问题，通过结构突变点前、后以及突变点处矩函数的变化，说明了模型如果确实存在着结构突变，忽略它，GMM方法估计量不但是有偏的而且是非一致的；重点介绍了Wachter(2004)的动态面板数据模型内生结构突变检验的理论框架，识别方法及其检验统计量的结构，讨论了其检验统计量的有限样本性质；使用Wachter(2004)的动态面板数据模型的内生结构突变识别方法，对中国通货膨胀惯性模型和居民消费函数模型进行未知结构突变检验，根据检验结果，使用系统GMM估计方法对模型参数进行估计，并同未考虑结构突变的估计结果进行了比较。
基于上述，全文的创新和意义为：(1)不同于现有文献，本文从数据生成过程对模型估计量的偏误性及有效性影响程度的角度出发，揭示了各种主要的动态面板数据模型估计方法的适用条件和估计量的统计性质；(2)本文设计并实现的仿真实验所使用的截面个数，为现有文献中对动态面板数据模型估计偏误仿真研究中最小的，其结果对于如何应用我国省际动态面板数据研究现实经济问题，具有针对性的意义；(3)系统地阐述了动态面板数据模型结构突变的检验及估计这一计量经济学的前沿问题，首次向国内引入允许模型的斜率系数及个体效应在未知时刻存在着结构突变，并且不约束后者的突变具有同质性的动态面板数据模型内生结构突变检验方法；(4)首次使用Wachter检验方法，对中国通货膨胀惯性和居民消费函数动态面板数据模型进行未知结构突变检验，结果显示两个模型都存在着显著的结构性变化，说明了在研究我国经济问题，进行计量分析时，对模型进行结构突变的检验，防止模型错误设定有着重要的意义。
The existing literatures show that panel data model has been an important branch of modern econometrics ever since proposed in 1960s,of which the dynamic panel data model is the forefront and research focus of this branch.The so-called Dynamic Panel Data Model, is the panel data model which reflects the dynamic hysteresis effect by introducing lagged response variables to the static model. The specificity of this model lies in that the lagged item of dependent variable is related to the individual effect of random error components, resulting in the estimator endogenous. Further, how to test the endogenous structural breaks of dynamic panel data model is undoubtedly the most difficult problems in this direction. This article focuses on the dynamic panel data model estimate and the endogenous structural breaks testing, and its research contents and conclusions can be summarized as following:
Firstly,this paper formulates thoroughly and systematically the estimator bias, the main estimate method and assumptions of GMM, as well as the weak instrumental variable and so on. On this basis, it reveals the statistical nature and application condition, the source of bias or weak effect of the major estimators of GMM,and how to correct. The major findings are that the various estimate methods of dynamic panel data model applies different data generation process; the estimator bias resulting from the system GMM is the smallest and most effective in most cases, but if the variance ratio of individual effects and idiosyncratic shocks is very small,especially when very large,the bias degree of estimator may be greater than the estimate results from differenced GMM; the bias degree of estimator from the differenced GMM increases significantly with the auto-regression coefficients close to 1, but its sensitivity to the changes of variance ratio is weaker;the estimator of GMM of the level equation shows larger sensitivity to the changes of variance ratio, the bias degree of estimator is smaller than the estimator of differenced GMM when the auto-regression coefficient is larger; The bias correction method based on the fixed effect bias estimator, especially the approach proposed by Hansen (2001) has the smallest degree of bias when there are no other predetermined variables in model.otherwise,the nature of limited samples if relatively poor.
Secondly, the basic feature of China's provincial-level panel data is that the largest useful number of cross-sectional is 31 .but in existing literatures, the simulation study of dynamic panel data model estimation bias under the framework of the GMM designs much greater cross-section numbers than the number of China's provinces,so that Chinese scholars have not a better measure of estimate bias when analysing China's provincial-level dynamic panel data. Considering all the factors effecting the estimated bias,the author designs and realizes the corresponding simulation study, and finds the bias degree of autoregression coefficient estimator with three representative dynamic panel data model on the condition that the cross-section number is 30. Based on simulation results, when doing dynamic model analysis in China's provincial-level panel data ,this paper recommends as following: the appropriate values of time dimensionis 10 to 15; for smaller auto-regression coefficient and larger variance ratio of individual effects and idiosyncratic shocks, applies differenced GMM;the variance ratio of individual effects and idiosyncratic shocks close to 1, using the system GMM;when the variance ratio of individual effects and idiosyncratic shocks is large,if the auto- regression coefficient is close to 0.5,the forward orthogonal deviation GMM applies;if close to 1, the system GMM applies;Unless the auto-regressive coefficients and the variance ratio of individual effects and idiosyncratic shocks are all very small, all three estimation methods do not use the potential instrumental variables at all.and it is recommended to select instrumental variables with lag periods less than 4.
Thirdly, this paper formulates thoroughly and systematically the structure breaks testing and estimation of dynamic panel data model, if there is indeed structural breaks, ignoring it,the estimator of GMM is not only biased but also non-consistent through the change of the moment function before and after, as well as breaks point. this paper Focuses on the endogenous structural breaks testing's theoretical framework, identification methods and the structure of test statistic of Wachter (2004)'s dynamic panel data model; And do unknown structural breaks test to the model of inflation inertia in Wachter (2004)'s endogenous structural breaks test method of dynamic panel data model. in accordance with the test results, this paper estimates the model parameters in the system GMM and compares to the estimator without considering structural breaks.
Based on the above, this paper's innovation and meaning lie in: firstly,from the influence of data generation process to estimator bias and validity, this article reveals the statistical nature and application condition of major dynamic panel data model estimator,and this is different from the existing literature;secondly,the cross-section number in the simulation study designed and realized is the least in the existing literature on dynamic panel data model's estimator bias simulation study.And the result shows how to study the economic problems in China's provincial-level panel data;thirdly,this paper firstly introduces the endogenous structural breaks test method of dynamic panel data model method which allowing the slope coefficient and the individual effects of unknown time to be structural breaks ,and the later to be of the endogenous;forthly,do unknown structural breaks test to the model of inflation inertia and inhabitants consumption function in Wachter(2004) test method firstly. The result is that there are all significant structural changes in the two models. So it is much of significance to do structural breaks test to prevent the wrong model-setup in the study of China's economic problems with econometrics analysis.
引文
[1] Ahn, S. C. and H. Lee, GMM Estimation of Linear Panel Data Models with Time-Varying Individual Effects Journal of Econometrics, 2001, 101: 219-255.
[2] Ahn, S. C. and P. Schmidt, Efficient Estimation of Models for Dynamic Panel Data, Journal of Econometrics, 1995, 68: 5-27.
[3] Ahn, S. C. and P. Schmidt, Efficient Estimation of Dynamic Panel Data Models: Alternative Assumptions and Simplified Estimation, Journal of Econometrics,1997,76:309-321.
[4] Alonso-Borrego, C. and M. Arellano, Symmetrically normalized instrumental variable estimation using panel data, Journal of Business and Economic Statistics,1999,17: 36-49.
[5] Altissimo, F. and M. Ehrmann, Inflation Persistence and Price-setting Behavior in the Euro Area: A Summary of the Inflation Persistence Network Evidence, IPN Working Paper, 2006: 95.
[6] Alvarez, J. and M.. Arellano, The Time Series and Cross-Section, Asymptotics of Dynamic Panel Data Estimators, Econometrica, 2003, 71: 1121-1159.
[7] Amemiya, T. and T. E. MaCurdy, Instrumental-variable Estimation of an Error-components model, Econometrica, 1986, 54: 869-881.
[8] Anderson, T.W. and C. Hsiao, Estimation of Dynamic Models with Error Components, Journal of the American Statistical Association, 1981, 76: 598-606.
[9] Anderson, T. W. and C. Hsiao, Formulation and Estimation of Dynamic Models using Panel Data, Journal of Econometrics, 1982, 18: 47-82 .
[10] Andreas, B., A Comparison of Dynamic Panel Data Estimators: Monte Carlo Evidence and an Application to The Investment Function, Discussion paper 05/03, Economic Research Centre of the Deutsche Bundesbank, 2003
[11] Andrews, D. W. K., Tests for Parameter Instability and Structural Change with Unknown Change Point, Econometrica, 1993, 61: 821-856.
[12] Andrews, D. W. K., Tests for Parameter Instability and Structural Change with Unknown Change Point, Cowles Foundation Discussion Paper No. 943, Yale University, 1989c.
[13] Andrews. D. W. K. and B. Lu, Consistent model and Moment selection Procedures for GMM Estimation with Application to Dynamic Panel Data models, Journal of Econometrics, 2001,101: 123-164.
[14] Andrews, D. W. K. and R. C. Fair, Inference in Nonlinear Econometric Models with Structural Change, Review of Economic Studies, 1988, 55: 615-640.
[15] Angrist, J. D., G. W. Imbens and A. Krueger, Jackknife Instrumental Variables Estimation, Journal of Applied Econometrics, 1999, 14: 57-67.
[16] Arellano, M., A note on the Anderson-Hsiao estimator for panel data, Economics Letters, 1989,31:337-341.
[17] Arellano, M., Modelling Optimal Instrumental Variables for Dynamic Panel Data Models, CEMFI Working Paper no. 0310,2002.
[18] Arellano, M., Panel Data Econometrics, Oxford University Press, Oxford, 2003.
[19] Arellano, M., Testing for Autocorrelation in Dynamic Random Effects Models,Review of Economic Studies, 1990, 57: 127-134.
[20] Arellano, M. and O. Bover, Another Look at the Instrumental Variable Estimation of Error-Components Models, Journal of Econometrics, 1995, 68: 29-51.
[21] Arellano, M. and S. R. Bond , Dynamic Panel Data Estimation using DPD98 for GAUSS , mimeo , Institute for Fiscal Studies , London ( http://www.ifs.org.uk /staff/steve_b.shtml), 1998.
[22] Arellano, M. and S. R. Bond, Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations, Review of Economic Studies, 1991,58:277-297.
[23] Attanasio, O. P., L. Picci and A. Scorcu, Saving, Growth and Investment: A Macroeconomic Analysis Using a Panel of Countries, Review of Economics and Statistics, 2000, 82: 182-211.
[24] Balestra, P. and M. Nerlove, Pooling Cross Section and Time Series Data in the Estimation of a Dynamic Model: The Demand for Natural Gas, Econometrica, 1966,34: 585-612.
[25] Bai J., Estimation of Structural Change in Econometric Models, unpublished manuscript, Department of Economics, University of California, Berkeley, 1991.
[26] Bai. J., and P. Perron, Estimating and Testing Linear Models with Multiple Structural Changes, Econometrica, 1998, 66: 47-48.
[27] Behr, A. and E. Bellgardt, Dynamic Q-investment Functions for Germany Using Panel Balance Sheet Data and a New Algorithm for the Capital Stock at Replacement Values, Discussion Paper 23/02, Deutsche Bundesbank, 2002.
[28] Bekker, P. A., Alternative Approximations to the Distributions of Instrumental Variable Estimators, Econometrica, 1994,62: 657-681.
[29] Bhargava, A. and J. D. Sargan, Estimating Dynamic Random Effects Models from Panel Data Covering Short Time Periods, Econometrica, 1983,51: 1635-1659.
[30] Blundell, R. and S. Bond, Initial Conditions and Moment Restrictions in Dynamic Panel Data Models, working Paper No.W95 /17, The Institute for Fiscal Studies,London, 1995.
[31] Blundell, R. W. and S. R. Bond, Initial Conditions and Moment Restrictions in Dynamic Panel Data Models, Journal of Econometrics, 1998, 87: 115-143.
[32] Blundell, R. W., S. R. Bond and F. Windmeijer, Estimation in Dynamic Panel Data Models: Improving on the Performance of the Standard GMM Estimator in B. Baltagi(ed) Advances in Econometrics, Volume 15: Nonstationary Panels, Panel Cointegration, and Dynamic Panels, Advances in Econometrics 15, JAI Press, JAI Elsevier Science, 2000.
[33] Blundell, R. W. and R.. J. Smith, Initial Conditions and Efficient Estimation in Dynamic Panel Data Models, Annales d Economie et de Statistique 20/21, 1991:109-123.
[34] Blundell, R., S. Bond and F. Windmeijer, Estimation in Dynamic Panel Data Models: Improving on the Performance of the Standard GMM Estimator, in B.H. Baltagi (ed.), Nonstationary Panels, Panel Cointegration and Dynamic Panels Vol.5,pp.53-91, Elsevier Science, Amsterdam, 2000.
[35] Bond, S., Dynamic Panel Data Models, A Guide to Micro Data Methods and Practice, Working Paper 09/02, Institute for Fiscal Studies, London, 2002.
[36] Bound, J., D. A. Jaeger and R.. M. Baker, Problems with Instrumental Variables Estimation when the Correlation Between the Instruments and the Endogenous Explanatory Variable is Weak, Journal of the American Statistical Association,1995,90:443-450.
[37] Bowsher, C, On Testing Overidentifying Restrictions in Dynamic Panel Data Models, Nuffield College Discussion Paper in Economics, no. 2000-W28, 2000.
[38] Bowsher, C. G., On Testing Overidentifying Restrictions in Dynamic Panel Data Models, Economics, 2002, 77: 211-20.
[39] Breusch, T. S., G. E. Mizon and P. Schmidt, Efficient Estimation using Panel Data, Econometrica, 1989, 57: 695-701.
[40] Bun, M. J. G. and J. F. Kiviet, The Effects of Dynamic Feedbacks on LS and MM Estimator Accuracy in Panel Data Models, Journal of Econometrics, 2006, 132:409-444.
[41] Calvo, A., Staggered Prices in a Utility-maximizing Framework, Journal of Monetary Economics, 1983, 9: 383-398.
[42] Carrie, A., Falls, Specification Tests In Panel Data Models with Unobserved Effects and Endogenous Explanatory Variables, A Dissertation, 2007.
[43] Carroll, C. D. and D. N. Weil, Saving and Growth: A Reinterpretation, Carnegie-Rochester Conference Series on Public Policy, 1994,40: 133-192.
[44] Challlberlain, G., Panel Data, in: Z. Griliches and M. D. Intriligator, eds, Handbook of Econometrics , VOL (Ⅱ) (Elsevier, Amsterdam), 1984.
[45] Chamberlain, G., Multivariate Regression Models for Panel Data, Journal of Econometrics, 1982,18: 5-46.
[46] Chamberlain, G., Panel data, in: Z. Griliches and M.D. Intriligator, eds., Handbook of econometrics, Vol. 2 (Elsevier, Amsterdam), 1984: 1247-1313.
[47] Chamberlain, G., Comment: Sequential Moment Restrictions in Panel Data, Journal of Business and Economic Statistics, 1992b, 10: 20-26.
[48] Chiara, O., Panel Unit Root and Panel Cointegration Methods, European Central Bank, September 24, 2004.
[49] David, R., How to Do xtabond2: An Introduction to "Difference" and "System" GMM in Stata, Working Paper Number 103, 2006, 12.
[50] Donald, W. and K. Andrews, Tests for Parameter Instability and Structural Change with Unknown Change Point, Econometrica, 1993, 61(4): 821-856.
[51] Donald W., K. Andrews and B. Lu, Consistent model and moment Selection Criteria for GMM Estimation with Application to Dynamic Panel Data models, 1998.
[52] Fuhrer, J., and G. Moore, Inflation Persistence, Quarterly Journal of Economics,1995,109: 127-159.
[53] Godfrey, L. G, Testing for Serial Correlation by Variable Addition in Dynamic Models Estimated by Instrumental Variables, The Review of Economics and statistics, 1994, 76: 550-559.
[54] Hahn, J., J. Hausman and G. Kuersteiner, Bias Corrected Instrumental Variables Estimation for Dynamic Panel Models with Fixed Effects, MIT Department of Economics Working Paper No. 01-24, 2002.
[55] Hahn, J. and A. Inoue, A Monte Carlo Comparison of Various Asymptotic Approximations to the Distribution of Instrumental Variables Estimators, Econometric Reviews, 2002, 21: 309-336.
[56] Hahn, J. and G. Kuersteiner, Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects when both n and T are Large, Econometrica, 2002,70: 1639-1657.
[57] Hansen, G., A Bias-corrected Least Squares Estimator of Dynamic Panel Models, Allgemeines Statistisches Archiv, 2001, 85: 127-140.
[58] Hansen. C, J. Hausman and W. Newey, Estimation with Many Instrumental Variables, Unpublished manuscript, 2005.
[59] Haque, N. U., M. H. Pesaran, and S. Sharma, Negelected Heterogeneity and Dynamics in Cross-Country Savings Regressions, in J. Krishnakumar and E. Ronchetti(eds): Panel Data Econometrics-Future Directions: Papers in Honour of Professor Balesrra, Elsevier Science. 2000.
[60] Hausman, J. H., An Instrumental Variable Approach to Full-Information Estimators of Linear and Certain Nonlinear Econometric Models, Econometrica, 1975, 43:727-738.
[61] Hausman. J. A. and W. E. Taylor, Panel data and unobservable individual effects, Econometrica, 1981,49: 1377-1398.
[62] Hayakawa, K., Small Sample Bias Properties of the System GMM Estimator in Dynamic Panel Data Models, Hi-Stat Discussion Paper Series d05-82, Institute of Economic Research, Hitotsubashi University, 2005.
[63] Hayakawa, K., The Asymptotic Properties of the System GMM Estimator in Dynamic Panel Data Models when both N and T are large, Hi-Stat Discussion Paper No. 129. Hitotsubashi University, 2006.
[64] Hayakawa, K., Small Sample Bias Properties of the System GMM Estimator in Dynamic Panel Data Models, Discussion Paper Series No.82, 2005, 4.
[65] Hayakawa, K., Efficient GMM Estimation of Dynamic Panel Data Models Where Large Heterogeneity May Be Present, Discussion Paper Series, No. 130, 2006, 1.
[66] Holtz-Eakin, D., W. Newey and H. S. Rosen, Estimating Vector Autoregressions with Panel Data, Econometrica, 1988, 56: 1371-1395.
[67] Hsiao, C, Analysis of Panel Data, Cambridge: Cambridge University Press, 1986.
[68] Im, K. S., M. H. Pesaran and Y. Shin, Testing for Unit Roots in Heterogeneous Panels, DAE, Working Paper 9526, University of Cambridge, 1997.
[69] Im, K.S., M. H. Pesaran and Y. Shin, Testing for Unit Roots in Heterogeneous Panels, Journal of Econometrics, 2003, 1(15): 53-74.
[70] Judson, R. A. and A. L. Owen., Estimating dynamic panel models, A practical guide for macroeconomists Economics, 1999, 65: 9-15.
[71] Keane, M. P. and D. E. Runkle, On the Estimation of Panel-Data Models with Serial Correlation when Instruments Are Not Strictly Exogenous, Journal of Business and Econo- mics Statistics, 1992, 10: 1-9.
[72] Kiviet, J. F., On bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models, Journal of Econometrics, 1995, 68: 53-78.
[73] Kiviet, J. F., Expectations of Expansions for Estimators in a Dynamic Panel Data model; Some Results for Weakly Exogenous Regressors, In: Hsiao, C, Lahiri, K., Lee, L.F., Pesaran, M.H. (Eds.), Analysis of Panels and Limited Dependent Variables, Cambridge University Press, Cambridge, 1999.
[74] Kramer, W, W. Ploberger, and R. Alt, Testing for Structural Change in Dynamic Models, Econometrica, 1988, 56: 1355-1369.
[75] Kruiniger, H., GMM Estimation of Dynamic Panel Data Models with Persistent Data, QMW, University of London, Dept. of Economics Working Paper Series, 2000:428.
[76] Lee, M., Efficiency Gains of System GMM and MDE over Individual Equation Estimation, The Japanese Economic Review, 2004, 55(4): 12.
[77] Levin, A. and J. Piger, Explicit Inflation Objectives and Macroeconomic Outcomes, European Central Bank Working Paper, 2004: 383.
[78] Masson P. R., T. Bayoumi and H. Samiei, International Evidence on the Determinants of Private Saving, World Bank Economic Review, 1998, 12: 483-501.
[79] Maurice J. G. and B. F. Windmeijer, The Weak Instrument Problem of the System GMM Estimator in Dynamic Panel Data Models, Discussion Paper No. 07/595,2007, 3.
[80] Maurice, J., G. Bun and J. F. Kiviet, The Effects of Dynamic Feedbacks on LS and MM estimator Accuracy in Panel Data Models, Journal of Econometrics, 132,2006:409-444.
[81] McCallum, T., A Semi-classical Model of Price-level Adjustment, Carnegie-Rochester Conference Series on Public Policy, 1994,41: 251-284.
[82] Metcalf, G., Specification Testing in Panel Data with Instrumental Variables, Journal of Econometrics, 1996, 71: 291-307.
[83] Nelson, C. R. and R. Startz, Some Further Results on the Exact Small Sample Properties of the Instrumental Variables Estimator, Econometrica, 1990(a), 58:967-976.
[84] Nelson, C. R and R.. Startz, The Distribution of the Instrumental Variable Estimator and its t Ratio when the Instrument is a Poor One, Journal of Business, 1990b, 63: S125-140.
[85] Nickell, S., Biases in Dynamic Models with Fixed Effects. Econometrica, 1981, 49(6): 417-26.
[86] Okui, R.,The optimal Choice of Moments in Dynamic Panel Data Models, unpublished manuscript, 2004.
[87] Okui, R., Instrumental Variable Estimation in the Presence of Many Moment Conditions, unpublished manuscript, 2005a.
[88] Okui, R., The Optimal Choice of Moments in Dynamic Panel Data Models, unpublished manuscript, 2005b.
[89] Hall, R. Stochastic Implications of the Life Cycle-Permanent Income Hypothesis: Theory and Evidence, Journal of Political Economy, 1978, 5: 971-987.
[90] Reilly, G. and K. Whelanh, as Euro-Area Inflation Persistence Changed Over Time, Review of Economics and Statistics, 2005, 4: 807-820.
[91] Ridder, G. and T. Wansbeek, Dynamic Models for Panel Data, in F. van der Ploeg (ed.), Advanced Lectures in Quantitative Economics, Academic Press, 1990.
[92] Sargan, J., The Estimation of Economic Relationships Using Instrumental Variables, Econometrica, 1958, 26(3): 393-415.
[93] Sargan, J. D., Testing for Misspecification after Estimation Using Instrumental Variables, in: E. Maasoumi (ed.), Contributions to Econometrics: John Denis Sargan, Volume (Ⅰ) , Cambridge University Press, 1988.
[94] Schmidt-Hebbel, K. and L. Serv(?)n, Saving in the World: The Stylized Facts, in K. Schmidt-Hebbel and L. Serven (eds.), The Economics of Saving and Growth: Theory, Evidence, and Implications for Policy, Cambridge University Press, 1999:6-32.
[95] Staiger, D. and J. H. Stock, Instrumental Variables Regression with Weak Instruments, Econometrica, 1997, 65: 557-586.
[96] Stephen, G, Has the Inflation Process Changed, Economic Policy, 2006,4: 311-352.
[97] Stephen, R. and S. Bond, Dynamic Panel Data Models: A Guide to Micro Data Methods and Practice, cemmap working paper CWP09/02, 4, 2002.
[98] Taylor, J., Aggregate Dynamics and Staggered Contracts, Journal of Political Economy, 1980, 88: 1-23.
[99] Tzavalis,E.,Structural Breaks and Unit Root Test for Short Panel,mimeo,Queen Mary,University of London,2002, 30
[100] Wachter, D. and E. Tzavalis, Detection of Structural Breaks in Linear Dynamic Panel Data Models QM University of London working paper, 2004, 12: 2-49.
[101] Wachter, D. and E. Tzavalis, Monte Carlo Comparison of Model and Moment Selection and Classical Inference Approaches to Break Detection in Panel Data Economics Letters, 2005, 88: 91-96.
[102] White, H., Instrumental Variables Regression with Independent Observations Econometrica, 1982, 50: 483-499.
[103]Windmeijer, F., A finite Sample Correction for the Variance of Linear efficient two-step GMM Estimators, Journal of Econometrics, 2005, 126:25-51.
[104]Wooldridge, J, Instrumental Variables Estimation with Panel Data, Econometric Theory, 2005, 21: 865-869.
[105]白仲林，尹长斌，中国省际全要素生产率动态行为的经验研究．西北师大学报，2008(7)：118-123．
[106]封福育，王少平．外商直接投资对中国国内投资的影响．统计与决策，2006，5(下)：93-94．
[107]杭斌．基于持久收入和财富目标的跨时消费选择．统计研究，2007(2)：38-43．
[108]李琴，李大胜，余建斌．地方财政支农、农村税费改革与农户投资--基于26个省动态面板数据的实证分析．南开经济研究，2008(1)：133-142．
[109]李文星，徐长生，艾春荣．中国人口年龄结构和居民消费：1989-2004．经济研究，2008(7)：118-129．
[110]连玉君，苏治．上市公司现金持有：静态权衡还是动态权衡．世界经济，2008(10)：84-96．
[111]刘长庚，吕志华．改革开放以来我国居民边际消费倾向的实证研究．消费经济，2005(8)：44-47．
[112]柳建光，李子奈．关于投资乘数及边际消费倾向计算方法的讨论．统计研究，2006(12)：66-68．
[113]刘小铭．我国粮食价格与居民消费价格关系研究．经济问题探索，2008，4．
[114]卢锋，彭凯翔．中国粮价与通货膨胀关系(1987-1999)．经济学(季刊)，2002：1-4．
[115]彭方平，王少平．我国利率政策的微观效应--基于动态面板数据模型研究．管理世界，2007(1)：24-29．
[116]彭方平，王少平，吴强．我国经济增长的多重均衡现象--基于动态门槛面板数据模型的研究．经济学，2007(7)：1041-1052．
[117]盛斌，马涛．中间产品贸易对中国劳动力需求变化的影响：基于工业部门动态面板数据的分析．世界经济，2008(3)：12-20．
[118]王秀清，钱小平．1981-2000年中国农产品价格上涨的波及效应．中国农村经济，2004
[119]吴泰岳，李慧，张鹏．粮食价格与居民消费价格关系的统计分析：1997．1-2005．4年粮价与物价的实证分析．数学的实践与认识，2006．
[120]钱放．中国通货膨胀的持续性及其与利率的关系．金融研究，1997(7)：21-23．
[121]王少平，封福育．外商直接投资对中国贸易的效应与区域差异：基于动态面板数据模型的分析．世界经济，2006(8)：23-30．
[122]武大勇，张伟，姜凌．变系数动态面板数据模型的半参数估计．数量经济技术经济研究，2008(2)：142-148．
[123]杨继生，王少平．非线性动态面板模型的条件GMM估计．数量经济技术经济研究，2008(12)：149-155．
[124]袁志刚，宋铮．城镇居民消费行为变异与我国经济增长．经济研究，1999(11)：20-28
[125]张成思．中国通货膨胀特征与货币启示．经济研究，2008(2)：33-43．
[126]郑月明，董登新．外商直接投资对我国就业的区域差异与动态效应--基于动态面板数据模型的分析．数量经济技术经济研究，2008(5)：104-113．
[2] Ahn, S. C. and P. Schmidt, Efficient Estimation of Models for Dynamic Panel Data, Journal of Econometrics, 1995, 68: 5-27.
[3] Ahn, S. C. and P. Schmidt, Efficient Estimation of Dynamic Panel Data Models: Alternative Assumptions and Simplified Estimation, Journal of Econometrics,1997,76:309-321.
[4] Alonso-Borrego, C. and M. Arellano, Symmetrically normalized instrumental variable estimation using panel data, Journal of Business and Economic Statistics,1999,17: 36-49.
[5] Altissimo, F. and M. Ehrmann, Inflation Persistence and Price-setting Behavior in the Euro Area: A Summary of the Inflation Persistence Network Evidence, IPN Working Paper, 2006: 95.
[6] Alvarez, J. and M.. Arellano, The Time Series and Cross-Section, Asymptotics of Dynamic Panel Data Estimators, Econometrica, 2003, 71: 1121-1159.
[7] Amemiya, T. and T. E. MaCurdy, Instrumental-variable Estimation of an Error-components model, Econometrica, 1986, 54: 869-881.
[8] Anderson, T.W. and C. Hsiao, Estimation of Dynamic Models with Error Components, Journal of the American Statistical Association, 1981, 76: 598-606.
[9] Anderson, T. W. and C. Hsiao, Formulation and Estimation of Dynamic Models using Panel Data, Journal of Econometrics, 1982, 18: 47-82 .
[10] Andreas, B., A Comparison of Dynamic Panel Data Estimators: Monte Carlo Evidence and an Application to The Investment Function, Discussion paper 05/03, Economic Research Centre of the Deutsche Bundesbank, 2003
[11] Andrews, D. W. K., Tests for Parameter Instability and Structural Change with Unknown Change Point, Econometrica, 1993, 61: 821-856.
[12] Andrews, D. W. K., Tests for Parameter Instability and Structural Change with Unknown Change Point, Cowles Foundation Discussion Paper No. 943, Yale University, 1989c.
[13] Andrews. D. W. K. and B. Lu, Consistent model and Moment selection Procedures for GMM Estimation with Application to Dynamic Panel Data models, Journal of Econometrics, 2001,101: 123-164.
[14] Andrews, D. W. K. and R. C. Fair, Inference in Nonlinear Econometric Models with Structural Change, Review of Economic Studies, 1988, 55: 615-640.
[15] Angrist, J. D., G. W. Imbens and A. Krueger, Jackknife Instrumental Variables Estimation, Journal of Applied Econometrics, 1999, 14: 57-67.
[16] Arellano, M., A note on the Anderson-Hsiao estimator for panel data, Economics Letters, 1989,31:337-341.
[17] Arellano, M., Modelling Optimal Instrumental Variables for Dynamic Panel Data Models, CEMFI Working Paper no. 0310,2002.
[18] Arellano, M., Panel Data Econometrics, Oxford University Press, Oxford, 2003.
[19] Arellano, M., Testing for Autocorrelation in Dynamic Random Effects Models,Review of Economic Studies, 1990, 57: 127-134.
[20] Arellano, M. and O. Bover, Another Look at the Instrumental Variable Estimation of Error-Components Models, Journal of Econometrics, 1995, 68: 29-51.
[21] Arellano, M. and S. R. Bond , Dynamic Panel Data Estimation using DPD98 for GAUSS , mimeo , Institute for Fiscal Studies , London ( http://www.ifs.org.uk /staff/steve_b.shtml), 1998.
[22] Arellano, M. and S. R. Bond, Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations, Review of Economic Studies, 1991,58:277-297.
[23] Attanasio, O. P., L. Picci and A. Scorcu, Saving, Growth and Investment: A Macroeconomic Analysis Using a Panel of Countries, Review of Economics and Statistics, 2000, 82: 182-211.
[24] Balestra, P. and M. Nerlove, Pooling Cross Section and Time Series Data in the Estimation of a Dynamic Model: The Demand for Natural Gas, Econometrica, 1966,34: 585-612.
[25] Bai J., Estimation of Structural Change in Econometric Models, unpublished manuscript, Department of Economics, University of California, Berkeley, 1991.
[26] Bai. J., and P. Perron, Estimating and Testing Linear Models with Multiple Structural Changes, Econometrica, 1998, 66: 47-48.
[27] Behr, A. and E. Bellgardt, Dynamic Q-investment Functions for Germany Using Panel Balance Sheet Data and a New Algorithm for the Capital Stock at Replacement Values, Discussion Paper 23/02, Deutsche Bundesbank, 2002.
[28] Bekker, P. A., Alternative Approximations to the Distributions of Instrumental Variable Estimators, Econometrica, 1994,62: 657-681.
[29] Bhargava, A. and J. D. Sargan, Estimating Dynamic Random Effects Models from Panel Data Covering Short Time Periods, Econometrica, 1983,51: 1635-1659.
[30] Blundell, R. and S. Bond, Initial Conditions and Moment Restrictions in Dynamic Panel Data Models, working Paper No.W95 /17, The Institute for Fiscal Studies,London, 1995.
[31] Blundell, R. W. and S. R. Bond, Initial Conditions and Moment Restrictions in Dynamic Panel Data Models, Journal of Econometrics, 1998, 87: 115-143.
[32] Blundell, R. W., S. R. Bond and F. Windmeijer, Estimation in Dynamic Panel Data Models: Improving on the Performance of the Standard GMM Estimator in B. Baltagi(ed) Advances in Econometrics, Volume 15: Nonstationary Panels, Panel Cointegration, and Dynamic Panels, Advances in Econometrics 15, JAI Press, JAI Elsevier Science, 2000.
[33] Blundell, R. W. and R.. J. Smith, Initial Conditions and Efficient Estimation in Dynamic Panel Data Models, Annales d Economie et de Statistique 20/21, 1991:109-123.
[34] Blundell, R., S. Bond and F. Windmeijer, Estimation in Dynamic Panel Data Models: Improving on the Performance of the Standard GMM Estimator, in B.H. Baltagi (ed.), Nonstationary Panels, Panel Cointegration and Dynamic Panels Vol.5,pp.53-91, Elsevier Science, Amsterdam, 2000.
[35] Bond, S., Dynamic Panel Data Models, A Guide to Micro Data Methods and Practice, Working Paper 09/02, Institute for Fiscal Studies, London, 2002.
[36] Bound, J., D. A. Jaeger and R.. M. Baker, Problems with Instrumental Variables Estimation when the Correlation Between the Instruments and the Endogenous Explanatory Variable is Weak, Journal of the American Statistical Association,1995,90:443-450.
[37] Bowsher, C, On Testing Overidentifying Restrictions in Dynamic Panel Data Models, Nuffield College Discussion Paper in Economics, no. 2000-W28, 2000.
[38] Bowsher, C. G., On Testing Overidentifying Restrictions in Dynamic Panel Data Models, Economics, 2002, 77: 211-20.
[39] Breusch, T. S., G. E. Mizon and P. Schmidt, Efficient Estimation using Panel Data, Econometrica, 1989, 57: 695-701.
[40] Bun, M. J. G. and J. F. Kiviet, The Effects of Dynamic Feedbacks on LS and MM Estimator Accuracy in Panel Data Models, Journal of Econometrics, 2006, 132:409-444.
[41] Calvo, A., Staggered Prices in a Utility-maximizing Framework, Journal of Monetary Economics, 1983, 9: 383-398.
[42] Carrie, A., Falls, Specification Tests In Panel Data Models with Unobserved Effects and Endogenous Explanatory Variables, A Dissertation, 2007.
[43] Carroll, C. D. and D. N. Weil, Saving and Growth: A Reinterpretation, Carnegie-Rochester Conference Series on Public Policy, 1994,40: 133-192.
[44] Challlberlain, G., Panel Data, in: Z. Griliches and M. D. Intriligator, eds, Handbook of Econometrics , VOL (Ⅱ) (Elsevier, Amsterdam), 1984.
[45] Chamberlain, G., Multivariate Regression Models for Panel Data, Journal of Econometrics, 1982,18: 5-46.
[46] Chamberlain, G., Panel data, in: Z. Griliches and M.D. Intriligator, eds., Handbook of econometrics, Vol. 2 (Elsevier, Amsterdam), 1984: 1247-1313.
[47] Chamberlain, G., Comment: Sequential Moment Restrictions in Panel Data, Journal of Business and Economic Statistics, 1992b, 10: 20-26.
[48] Chiara, O., Panel Unit Root and Panel Cointegration Methods, European Central Bank, September 24, 2004.
[49] David, R., How to Do xtabond2: An Introduction to "Difference" and "System" GMM in Stata, Working Paper Number 103, 2006, 12.
[50] Donald, W. and K. Andrews, Tests for Parameter Instability and Structural Change with Unknown Change Point, Econometrica, 1993, 61(4): 821-856.
[51] Donald W., K. Andrews and B. Lu, Consistent model and moment Selection Criteria for GMM Estimation with Application to Dynamic Panel Data models, 1998.
[52] Fuhrer, J., and G. Moore, Inflation Persistence, Quarterly Journal of Economics,1995,109: 127-159.
[53] Godfrey, L. G, Testing for Serial Correlation by Variable Addition in Dynamic Models Estimated by Instrumental Variables, The Review of Economics and statistics, 1994, 76: 550-559.
[54] Hahn, J., J. Hausman and G. Kuersteiner, Bias Corrected Instrumental Variables Estimation for Dynamic Panel Models with Fixed Effects, MIT Department of Economics Working Paper No. 01-24, 2002.
[55] Hahn, J. and A. Inoue, A Monte Carlo Comparison of Various Asymptotic Approximations to the Distribution of Instrumental Variables Estimators, Econometric Reviews, 2002, 21: 309-336.
[56] Hahn, J. and G. Kuersteiner, Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects when both n and T are Large, Econometrica, 2002,70: 1639-1657.
[57] Hansen, G., A Bias-corrected Least Squares Estimator of Dynamic Panel Models, Allgemeines Statistisches Archiv, 2001, 85: 127-140.
[58] Hansen. C, J. Hausman and W. Newey, Estimation with Many Instrumental Variables, Unpublished manuscript, 2005.
[59] Haque, N. U., M. H. Pesaran, and S. Sharma, Negelected Heterogeneity and Dynamics in Cross-Country Savings Regressions, in J. Krishnakumar and E. Ronchetti(eds): Panel Data Econometrics-Future Directions: Papers in Honour of Professor Balesrra, Elsevier Science. 2000.
[60] Hausman, J. H., An Instrumental Variable Approach to Full-Information Estimators of Linear and Certain Nonlinear Econometric Models, Econometrica, 1975, 43:727-738.
[61] Hausman. J. A. and W. E. Taylor, Panel data and unobservable individual effects, Econometrica, 1981,49: 1377-1398.
[62] Hayakawa, K., Small Sample Bias Properties of the System GMM Estimator in Dynamic Panel Data Models, Hi-Stat Discussion Paper Series d05-82, Institute of Economic Research, Hitotsubashi University, 2005.
[63] Hayakawa, K., The Asymptotic Properties of the System GMM Estimator in Dynamic Panel Data Models when both N and T are large, Hi-Stat Discussion Paper No. 129. Hitotsubashi University, 2006.
[64] Hayakawa, K., Small Sample Bias Properties of the System GMM Estimator in Dynamic Panel Data Models, Discussion Paper Series No.82, 2005, 4.
[65] Hayakawa, K., Efficient GMM Estimation of Dynamic Panel Data Models Where Large Heterogeneity May Be Present, Discussion Paper Series, No. 130, 2006, 1.
[66] Holtz-Eakin, D., W. Newey and H. S. Rosen, Estimating Vector Autoregressions with Panel Data, Econometrica, 1988, 56: 1371-1395.
[67] Hsiao, C, Analysis of Panel Data, Cambridge: Cambridge University Press, 1986.
[68] Im, K. S., M. H. Pesaran and Y. Shin, Testing for Unit Roots in Heterogeneous Panels, DAE, Working Paper 9526, University of Cambridge, 1997.
[69] Im, K.S., M. H. Pesaran and Y. Shin, Testing for Unit Roots in Heterogeneous Panels, Journal of Econometrics, 2003, 1(15): 53-74.
[70] Judson, R. A. and A. L. Owen., Estimating dynamic panel models, A practical guide for macroeconomists Economics, 1999, 65: 9-15.
[71] Keane, M. P. and D. E. Runkle, On the Estimation of Panel-Data Models with Serial Correlation when Instruments Are Not Strictly Exogenous, Journal of Business and Econo- mics Statistics, 1992, 10: 1-9.
[72] Kiviet, J. F., On bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models, Journal of Econometrics, 1995, 68: 53-78.
[73] Kiviet, J. F., Expectations of Expansions for Estimators in a Dynamic Panel Data model; Some Results for Weakly Exogenous Regressors, In: Hsiao, C, Lahiri, K., Lee, L.F., Pesaran, M.H. (Eds.), Analysis of Panels and Limited Dependent Variables, Cambridge University Press, Cambridge, 1999.
[74] Kramer, W, W. Ploberger, and R. Alt, Testing for Structural Change in Dynamic Models, Econometrica, 1988, 56: 1355-1369.
[75] Kruiniger, H., GMM Estimation of Dynamic Panel Data Models with Persistent Data, QMW, University of London, Dept. of Economics Working Paper Series, 2000:428.
[76] Lee, M., Efficiency Gains of System GMM and MDE over Individual Equation Estimation, The Japanese Economic Review, 2004, 55(4): 12.
[77] Levin, A. and J. Piger, Explicit Inflation Objectives and Macroeconomic Outcomes, European Central Bank Working Paper, 2004: 383.
[78] Masson P. R., T. Bayoumi and H. Samiei, International Evidence on the Determinants of Private Saving, World Bank Economic Review, 1998, 12: 483-501.
[79] Maurice J. G. and B. F. Windmeijer, The Weak Instrument Problem of the System GMM Estimator in Dynamic Panel Data Models, Discussion Paper No. 07/595,2007, 3.
[80] Maurice, J., G. Bun and J. F. Kiviet, The Effects of Dynamic Feedbacks on LS and MM estimator Accuracy in Panel Data Models, Journal of Econometrics, 132,2006:409-444.
[81] McCallum, T., A Semi-classical Model of Price-level Adjustment, Carnegie-Rochester Conference Series on Public Policy, 1994,41: 251-284.
[82] Metcalf, G., Specification Testing in Panel Data with Instrumental Variables, Journal of Econometrics, 1996, 71: 291-307.
[83] Nelson, C. R. and R. Startz, Some Further Results on the Exact Small Sample Properties of the Instrumental Variables Estimator, Econometrica, 1990(a), 58:967-976.
[84] Nelson, C. R and R.. Startz, The Distribution of the Instrumental Variable Estimator and its t Ratio when the Instrument is a Poor One, Journal of Business, 1990b, 63: S125-140.
[85] Nickell, S., Biases in Dynamic Models with Fixed Effects. Econometrica, 1981, 49(6): 417-26.
[86] Okui, R.,The optimal Choice of Moments in Dynamic Panel Data Models, unpublished manuscript, 2004.
[87] Okui, R., Instrumental Variable Estimation in the Presence of Many Moment Conditions, unpublished manuscript, 2005a.
[88] Okui, R., The Optimal Choice of Moments in Dynamic Panel Data Models, unpublished manuscript, 2005b.
[89] Hall, R. Stochastic Implications of the Life Cycle-Permanent Income Hypothesis: Theory and Evidence, Journal of Political Economy, 1978, 5: 971-987.
[90] Reilly, G. and K. Whelanh, as Euro-Area Inflation Persistence Changed Over Time, Review of Economics and Statistics, 2005, 4: 807-820.
[91] Ridder, G. and T. Wansbeek, Dynamic Models for Panel Data, in F. van der Ploeg (ed.), Advanced Lectures in Quantitative Economics, Academic Press, 1990.
[92] Sargan, J., The Estimation of Economic Relationships Using Instrumental Variables, Econometrica, 1958, 26(3): 393-415.
[93] Sargan, J. D., Testing for Misspecification after Estimation Using Instrumental Variables, in: E. Maasoumi (ed.), Contributions to Econometrics: John Denis Sargan, Volume (Ⅰ) , Cambridge University Press, 1988.
[94] Schmidt-Hebbel, K. and L. Serv(?)n, Saving in the World: The Stylized Facts, in K. Schmidt-Hebbel and L. Serven (eds.), The Economics of Saving and Growth: Theory, Evidence, and Implications for Policy, Cambridge University Press, 1999:6-32.
[95] Staiger, D. and J. H. Stock, Instrumental Variables Regression with Weak Instruments, Econometrica, 1997, 65: 557-586.
[96] Stephen, G, Has the Inflation Process Changed, Economic Policy, 2006,4: 311-352.
[97] Stephen, R. and S. Bond, Dynamic Panel Data Models: A Guide to Micro Data Methods and Practice, cemmap working paper CWP09/02, 4, 2002.
[98] Taylor, J., Aggregate Dynamics and Staggered Contracts, Journal of Political Economy, 1980, 88: 1-23.
[99] Tzavalis,E.,Structural Breaks and Unit Root Test for Short Panel,mimeo,Queen Mary,University of London,2002, 30
[100] Wachter, D. and E. Tzavalis, Detection of Structural Breaks in Linear Dynamic Panel Data Models QM University of London working paper, 2004, 12: 2-49.
[101] Wachter, D. and E. Tzavalis, Monte Carlo Comparison of Model and Moment Selection and Classical Inference Approaches to Break Detection in Panel Data Economics Letters, 2005, 88: 91-96.
[102] White, H., Instrumental Variables Regression with Independent Observations Econometrica, 1982, 50: 483-499.
[103]Windmeijer, F., A finite Sample Correction for the Variance of Linear efficient two-step GMM Estimators, Journal of Econometrics, 2005, 126:25-51.
[104]Wooldridge, J, Instrumental Variables Estimation with Panel Data, Econometric Theory, 2005, 21: 865-869.
[105]白仲林，尹长斌，中国省际全要素生产率动态行为的经验研究．西北师大学报，2008(7)：118-123．
[106]封福育，王少平．外商直接投资对中国国内投资的影响．统计与决策，2006，5(下)：93-94．
[107]杭斌．基于持久收入和财富目标的跨时消费选择．统计研究，2007(2)：38-43．
[108]李琴，李大胜，余建斌．地方财政支农、农村税费改革与农户投资--基于26个省动态面板数据的实证分析．南开经济研究，2008(1)：133-142．
[109]李文星，徐长生，艾春荣．中国人口年龄结构和居民消费：1989-2004．经济研究，2008(7)：118-129．
[110]连玉君，苏治．上市公司现金持有：静态权衡还是动态权衡．世界经济，2008(10)：84-96．
[111]刘长庚，吕志华．改革开放以来我国居民边际消费倾向的实证研究．消费经济，2005(8)：44-47．
[112]柳建光，李子奈．关于投资乘数及边际消费倾向计算方法的讨论．统计研究，2006(12)：66-68．
[113]刘小铭．我国粮食价格与居民消费价格关系研究．经济问题探索，2008，4．
[114]卢锋，彭凯翔．中国粮价与通货膨胀关系(1987-1999)．经济学(季刊)，2002：1-4．
[115]彭方平，王少平．我国利率政策的微观效应--基于动态面板数据模型研究．管理世界，2007(1)：24-29．
[116]彭方平，王少平，吴强．我国经济增长的多重均衡现象--基于动态门槛面板数据模型的研究．经济学，2007(7)：1041-1052．
[117]盛斌，马涛．中间产品贸易对中国劳动力需求变化的影响：基于工业部门动态面板数据的分析．世界经济，2008(3)：12-20．
[118]王秀清，钱小平．1981-2000年中国农产品价格上涨的波及效应．中国农村经济，2004
[119]吴泰岳，李慧，张鹏．粮食价格与居民消费价格关系的统计分析：1997．1-2005．4年粮价与物价的实证分析．数学的实践与认识，2006．
[120]钱放．中国通货膨胀的持续性及其与利率的关系．金融研究，1997(7)：21-23．
[121]王少平，封福育．外商直接投资对中国贸易的效应与区域差异：基于动态面板数据模型的分析．世界经济，2006(8)：23-30．
[122]武大勇，张伟，姜凌．变系数动态面板数据模型的半参数估计．数量经济技术经济研究，2008(2)：142-148．
[123]杨继生，王少平．非线性动态面板模型的条件GMM估计．数量经济技术经济研究，2008(12)：149-155．
[124]袁志刚，宋铮．城镇居民消费行为变异与我国经济增长．经济研究，1999(11)：20-28
[125]张成思．中国通货膨胀特征与货币启示．经济研究，2008(2)：33-43．
[126]郑月明，董登新．外商直接投资对我国就业的区域差异与动态效应--基于动态面板数据模型的分析．数量经济技术经济研究，2008(5)：104-113．