似无关回归模型及其应用研究
|
摘要
经典的似无关回归模型是指系统中的每个方程从表面上看起来是互不相关的,但是方程间扰动项的同期相关性却把各个方程紧密地联系在一起,其核心是通过利用样本信息和方程间扰动项的方差协方差矩阵的结构,来有效提高单方程OLS估计的精度,这即是GLS估计的思想。但是,GLS估计量的性质必须依赖于回归方程中解释变量的平稳性,如果解释变量是非平稳的并且具有内生性,那么GLS估计量将是有偏的,且服从非标准的极限分布。因此,我们首先必须检验系统中变量的平稳性与协整性。然而,由于现有的面板单位根和面板协整检验主要依赖于横截面相互独立的假定,对于横截面相关的情况则研究较少,因此,如何将似无关回归模型的思想应用于面板单位根与面板协整检验,使之包含横截面相关的情况则构成了这一方向的前沿问题。相应地,在SUR回归模型中,如何将平稳变量扩展为非平稳变量,进而将SUR模型扩展为似无关动态协整模型,也就构成了这一方向的另一前沿问题。如Mark et al(2005)等提出了似无关动态协整模型的思想及其估计方法,以此来消除解释变量的内生性影响,并获得了一个标准的渐近分布。
但是,在有限样本下,我们通过蒙特卡罗仿真发现,依据渐近理论所做的假设检验通常存在较为严重的水平扭曲现象,因此本文首先建议应用自举法来获得检验统计量的临界值,然后由此来形成可靠的检验结论。进一步,本文将SUR的回归思想应用于横截面相关的面板单位根和面板协整检验。由于目前大多数面板单位根检验在拒绝原假设时,所能得出的结论要么是所有截面均为单位根过程,要么是所有截面均为平稳过程,虽然Breuer et al(2001)建立的SUR-ADF型检验可以有效区分平稳与非平稳序列,但却容易受面板成员选择的影响,因此本文建议应用快速双自举的抽样方法,通过对SUR-ADF统计量计算双p值来提高该检验的稳健性。对于横截面相关的面板协整检验,本文建议按照传统EG两步法的思想,首先对模型进行面板协整的SUR估计,然后对残差进行面板单位根检验,如果我们不能拒绝面板平稳的原假设,即表明原方程为面板协整模型。
在实证应用方面,本文主要结合上述理论研究成果,将应用集中于对中国股市、区域资本流动性、环境质量对医疗支出影响的考察。本文将经典的似无关回归模型应用于考察股市收益与波动的相互关系,结果发现股票价格下降导致收益波动增加,股市收益与波动之间存在负向关系,即杠杆效应。在区域资本流动方面,考虑到地区之间的相关性与储蓄变量的内生性,本文应用似无关动态协整模型进行研究,发现我国东部为资本净流入区,资本的长期流入使得投资收益下降,还债能力降低;中部地区的储蓄基本上转化为本地区的投资,资本流动仅限于区域内部;西部为资本净流出地区,资本的流出导致了该地区长期经济增长缓慢。作为面板协整应用的一个例子,本文考察环境质量对医疗支出的影响情况,结果表明环境质量对医疗支出影响的长期弹性为正,说明环境污染的加剧导致人们健康状况的恶化,从而促进了医疗支出的增长。但是,从短期来看,环境质量对医疗支出的影响并不显著,这一结果解释了有些地方政府出于政绩考核的需要,在短期内会以牺牲环境为代价来换取经济的暂时发展,由此说明了做好环境保护工作的艰巨性与紧迫性。
本文从最经典的似无关回归模型出发,分理论和实证两个方面论述了该模型及其扩展形式在现实研究中的应用。由于SUR模型考虑了各个截面之间的潜在相关性,并且具有简单的一般化形式,因而它构成了计量经济学理论的基础,也突显了本文研究的现实意义。
Seemingly unrelated regression comes from the fact that each equation appears to be unrelated in a system. Nevertheless, correlation across the errors in different equations can provide links that can be exploited in estimation. It is well known that we can employ the sample information and error term’s variance-covariance matrix to improve the precision of estimation of parameters. That is the new, operational GLS best linear unbiased estimators of parameters of a set of regression equations. However, the efficient GLS estimators depend on some assumptions seriously, such as stationary regressors, and independent and identical errors. If the system of regression equations consists of nonstationary time series regression models that allow for endogenous regressors, the previous conclusion will be not true in general, and the GLS estimators will be biased and have nonstandard limit distributions. Consequently, we have to test whether the variables are nonstationary and then cointegration in a system. However, most of existing panel unit root tests and panel cointegration tests hinge critically on the assumption of cross-sectional independence. What about the results if we allow for the dependence across equations? So, it is an active field to extend such SUR methodology to panel unit root tests and panel cointegration tests that coincide with cross-sectional dependence. Another one is to change the stationary variables into nonstationary variables, and extend the SUR model to seemingly unrelated dynamic cointegrating regressions. For example, Mark et al(2005) develops this model and its DSUR estimators, to pure the effect of endogeneity. They also show that the DSUR estimators have asymptotically mixed normal distribution and the tests of parametric restrictions can be constructed by the Wald statistic which is asymptotically distributed as a chi-square variate.
However, we find using Monte Carlo methods that the tests may suffer from serious size distortion in finite samples, and suggest bootstrapping the tests to correct this inference problem. Furthermore, we extend the SUR method to panel unit root test and panel cointegration test to accommodate dependence across the cross-section. In most of recent papers, where unit root behavior is rejected, the conclusion reached is that all members of the panel are either stationary or nonstationary. Although the SUR-ADF test proposed by Breuer et al(2001) are informative about the behavior of each individual time series, it is highly sensitive to the selection of panel members. As such, this paper suggests fast double bootstrap to improve the reliability of SUR-ADF test by computing double p-values. As to the panel cointegration test under the cross-sectional dependence, we, according to the classical EG two steps, estimate the panel model by SUR at first, then conduct a panel unit root test to the residuals. If the null hypothesis of panel stationary can not be rejected, it is implied that the original models are panel cointegrated. Otherwise, it is much more favorable to spurious regression equations.
In empirical analysis, we, adopting the previous models, focus our attentions on the stock market, regional capital mobility and the effects of environmental quality on health expenditures. Firstly, we investigate the relationship between stock returns and return volatility in China, and find that the stock price decline raises the firm’s financial leverage, resulting in an increase in the volatility of equity. There is a negative statistical relation between current stock returns and changes in future stock return volatility, which is documented by leverage effect. Secondly, when we use the newly developed seemingly unrelated dynamic cointegrating regressions to examine the regional capital mobility, the results prove that China’s east section represents a net capital inflow, however, the sustaining inflow has reduced the investment return, which induces the long-run solvency declining. Meanwhile, the west section marks a net capital outflow, which should account for the creeping economic growth in this area, and the central section stays in a basically balanced position. Finally, this paper estimates the role of environmental quality in determining per capita health expenditures, which is taken as an example for panel cointegration application. Our empirical analysis reveals that the environmental quality exerts a statistically significant positive effect on health expenditures in the long run. The seriousness of environmental pollution results to the deterioration of people’s health status, which will induce an increase in health expenditures. While the fact of insignificant short-run impacts of environmental quality just provides some evidences for authorities pursuing economic growth, regardless of environmental conservation. This issue has become much more important from a policy point of view. Therefore, it is arduous, but also urgent to protect our environment.
Starting from the classical SUR model, this paper introduces its applications and developments both in theoretical and empirical analysis. As we know, the SUR model does not only allow for correlation across the errors in different equations, but have a simple and general exposition. It has become a basis of the econometric theory, which also underlines the great significance of this paper.
但是,在有限样本下,我们通过蒙特卡罗仿真发现,依据渐近理论所做的假设检验通常存在较为严重的水平扭曲现象,因此本文首先建议应用自举法来获得检验统计量的临界值,然后由此来形成可靠的检验结论。进一步,本文将SUR的回归思想应用于横截面相关的面板单位根和面板协整检验。由于目前大多数面板单位根检验在拒绝原假设时,所能得出的结论要么是所有截面均为单位根过程,要么是所有截面均为平稳过程,虽然Breuer et al(2001)建立的SUR-ADF型检验可以有效区分平稳与非平稳序列,但却容易受面板成员选择的影响,因此本文建议应用快速双自举的抽样方法,通过对SUR-ADF统计量计算双p值来提高该检验的稳健性。对于横截面相关的面板协整检验,本文建议按照传统EG两步法的思想,首先对模型进行面板协整的SUR估计,然后对残差进行面板单位根检验,如果我们不能拒绝面板平稳的原假设,即表明原方程为面板协整模型。
在实证应用方面,本文主要结合上述理论研究成果,将应用集中于对中国股市、区域资本流动性、环境质量对医疗支出影响的考察。本文将经典的似无关回归模型应用于考察股市收益与波动的相互关系,结果发现股票价格下降导致收益波动增加,股市收益与波动之间存在负向关系,即杠杆效应。在区域资本流动方面,考虑到地区之间的相关性与储蓄变量的内生性,本文应用似无关动态协整模型进行研究,发现我国东部为资本净流入区,资本的长期流入使得投资收益下降,还债能力降低;中部地区的储蓄基本上转化为本地区的投资,资本流动仅限于区域内部;西部为资本净流出地区,资本的流出导致了该地区长期经济增长缓慢。作为面板协整应用的一个例子,本文考察环境质量对医疗支出的影响情况,结果表明环境质量对医疗支出影响的长期弹性为正,说明环境污染的加剧导致人们健康状况的恶化,从而促进了医疗支出的增长。但是,从短期来看,环境质量对医疗支出的影响并不显著,这一结果解释了有些地方政府出于政绩考核的需要,在短期内会以牺牲环境为代价来换取经济的暂时发展,由此说明了做好环境保护工作的艰巨性与紧迫性。
本文从最经典的似无关回归模型出发,分理论和实证两个方面论述了该模型及其扩展形式在现实研究中的应用。由于SUR模型考虑了各个截面之间的潜在相关性,并且具有简单的一般化形式,因而它构成了计量经济学理论的基础,也突显了本文研究的现实意义。
Seemingly unrelated regression comes from the fact that each equation appears to be unrelated in a system. Nevertheless, correlation across the errors in different equations can provide links that can be exploited in estimation. It is well known that we can employ the sample information and error term’s variance-covariance matrix to improve the precision of estimation of parameters. That is the new, operational GLS best linear unbiased estimators of parameters of a set of regression equations. However, the efficient GLS estimators depend on some assumptions seriously, such as stationary regressors, and independent and identical errors. If the system of regression equations consists of nonstationary time series regression models that allow for endogenous regressors, the previous conclusion will be not true in general, and the GLS estimators will be biased and have nonstandard limit distributions. Consequently, we have to test whether the variables are nonstationary and then cointegration in a system. However, most of existing panel unit root tests and panel cointegration tests hinge critically on the assumption of cross-sectional independence. What about the results if we allow for the dependence across equations? So, it is an active field to extend such SUR methodology to panel unit root tests and panel cointegration tests that coincide with cross-sectional dependence. Another one is to change the stationary variables into nonstationary variables, and extend the SUR model to seemingly unrelated dynamic cointegrating regressions. For example, Mark et al(2005) develops this model and its DSUR estimators, to pure the effect of endogeneity. They also show that the DSUR estimators have asymptotically mixed normal distribution and the tests of parametric restrictions can be constructed by the Wald statistic which is asymptotically distributed as a chi-square variate.
However, we find using Monte Carlo methods that the tests may suffer from serious size distortion in finite samples, and suggest bootstrapping the tests to correct this inference problem. Furthermore, we extend the SUR method to panel unit root test and panel cointegration test to accommodate dependence across the cross-section. In most of recent papers, where unit root behavior is rejected, the conclusion reached is that all members of the panel are either stationary or nonstationary. Although the SUR-ADF test proposed by Breuer et al(2001) are informative about the behavior of each individual time series, it is highly sensitive to the selection of panel members. As such, this paper suggests fast double bootstrap to improve the reliability of SUR-ADF test by computing double p-values. As to the panel cointegration test under the cross-sectional dependence, we, according to the classical EG two steps, estimate the panel model by SUR at first, then conduct a panel unit root test to the residuals. If the null hypothesis of panel stationary can not be rejected, it is implied that the original models are panel cointegrated. Otherwise, it is much more favorable to spurious regression equations.
In empirical analysis, we, adopting the previous models, focus our attentions on the stock market, regional capital mobility and the effects of environmental quality on health expenditures. Firstly, we investigate the relationship between stock returns and return volatility in China, and find that the stock price decline raises the firm’s financial leverage, resulting in an increase in the volatility of equity. There is a negative statistical relation between current stock returns and changes in future stock return volatility, which is documented by leverage effect. Secondly, when we use the newly developed seemingly unrelated dynamic cointegrating regressions to examine the regional capital mobility, the results prove that China’s east section represents a net capital inflow, however, the sustaining inflow has reduced the investment return, which induces the long-run solvency declining. Meanwhile, the west section marks a net capital outflow, which should account for the creeping economic growth in this area, and the central section stays in a basically balanced position. Finally, this paper estimates the role of environmental quality in determining per capita health expenditures, which is taken as an example for panel cointegration application. Our empirical analysis reveals that the environmental quality exerts a statistically significant positive effect on health expenditures in the long run. The seriousness of environmental pollution results to the deterioration of people’s health status, which will induce an increase in health expenditures. While the fact of insignificant short-run impacts of environmental quality just provides some evidences for authorities pursuing economic growth, regardless of environmental conservation. This issue has become much more important from a policy point of view. Therefore, it is arduous, but also urgent to protect our environment.
Starting from the classical SUR model, this paper introduces its applications and developments both in theoretical and empirical analysis. As we know, the SUR model does not only allow for correlation across the errors in different equations, but have a simple and general exposition. It has become a basis of the econometric theory, which also underlines the great significance of this paper.
引文
[1] Andrews, D. W. K. Cross-Section Regression with Common Shocks. Econometrica, 2005, 73(5): 1551-1585.
[2] Avik, C. The Saving– Investment Relationship Revisited: New Evidence from Multivariate Heterogeneous Panel Cointegration Analyses. Journal of Comparative Economics, 2006, 34: 402-419.
[3] Bai, J. and Ng, S. A PANIC Attack on Unit Roots and Cointegration. Econometrica, 2004, 72: 1127-1177.
[4] Baltagi, B. H. Econometrics, 4th Edition. New York: Springer– Verlag, 2008.
[5] Banerjee, A., Marcellino, M. and Osbat, C. Some Cautions on the Use of Panel Methods for Integrated Series of Macro-Economic Data. Econometrics Journal, 2004, 7: 322-340.
[6] Banister, J. Population, Public Health and the Environment in China. The China Quarterly, 1998, 156: 986-1015.
[7] Bartels, R. and Feibig, D. G. A Simple Characterization of Seemingly Unrelated Regressions Models in Which OLS is BLUE. American Statistician, 1991, 45: 137-140.
[8] Bayoumi, T. Saving– Investment Correlations: Immobile Capital, Government Policy or Endogenous Behavior. IMF Staff Paper, 1990, 37: 360-387.
[9] Beran, R. Prepivoting Test Statistics: a Bootstrap View of Asymptotic Refinements. Journal of the American Statistical Association, 1988, 83: 687-697.
[10] Berenguer-Rico, V. and Carrion-I-Silvestre, J. L. Testing for Multicointegration in Panel Data with Common Factors. Oxford Bulletin of Economics and Statistics, 2006, Supplement, 68: 721-739.
[11] Berndt, E. R. The Practice of Econometrics: Classic and Contemporary. Addison– Wesley: Reading, MA, 1991.
[12] Bornhorst, F. and Baum, C. F. IPSHIN: Stata Module to Perform Im-Pesaran-Shin Panel Unit Root Test. Boston College, Department of Economics, Statistical Software Component, No.S419704, 2001.
[13] Breitung, J. and Das, S. Panel Unit Root Tests under Cross-Sectional Dependence. Statistica Neerlandica, 2005, 59(4): 414-433.
[14] Breuer, J. B., McNown, R. and Wallace, M. S. Misleading Inferences from Panel Unit-Root Tests with an Illustration from Purchasing Power Parity. Review of International Economics, 2001, 9(3): 482-493.
[15] Breuer, J. B., McNown, R. and Wallace, M. S. Misleading Inferences from Panel Unit Root Tests: a Reply. Review of International Economics, 2006, 14(3): 512-516.
[16] Breusch, T. S. and Pagan, A. R. The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics. Review of Economic Studies, 1980, 47: 239-253.
[17] Cerrato, M. and Sarantis, N. A Bootstrap Panel Unit Root Test under Cross-Sectional Dependence, with an Application to PPP. Computational Statistics & Data Analysis, 2007, 51: 4028-4037.
[18] Chang, Y. Nonlinear IV Unit Root Tests in Panels with Cross-Sectional Dependency. Journal of Econometrics, 2002, 110: 261-292.
[19] Chang, Y. Bootstrap Unit Root Tests in Panels with Cross-Sectional Dependency. Journal of Econometrics, 2004, 120: 263-293.
[20] Chang, Y., Park, J. Y. and Song, K. Bootstrapping Cointegrating Regressions. Journal of Econometrics, 2006, 133: 703-739.
[21] Cheung, Y.-W. and Ng, L. Stock Price Dynamics and Firm Size: an Empirical Investigation. Journal of Finance, 1992, 47: 1985-1997.
[22] Choi, I. Unit Root Tests for Panel Data. Journal of International Money and Finance, 2001, 20: 249-272.
[23] Choi, I. Combination Unit Root Tests for Cross-Sectionally Correlated Panels. Mimeo, Hong Kong University of Science and Technology, 2002.
[24] Christie, A. The Stochastic Behavior of Common Stock Variances: Value, Leverage, and Interest Rate Effects. Journal of Financial Economics, 1982, 10: 407-432.
[25] Coakley, J., Kulasi, F. and Smith, R. Current Account Solvency and the Feldstein-Horioka Puzzle. Economic Journal, 1996, 106: 620-627.
[26] Coakley, J., Kulasi, F. and Smith, R. The Feldstein-Horioka Puzzle and CapitalMobility: a Review. International Journal of Finance and Economics, 1998, 3: 169-188.
[27] Davidson, R. and MacKinnon, J. G. The Size Distortion of Bootstrap Tests. Econometric Theory, 1999, 15: 361-376.
[28] Davidson, R. and MacKinnon, J. G. Econometric Theory and Methods. New York: Oxford University Press, 2004.
[29] Davidson, R. and MacKinnon, J. G. The Power of Bootstrap and Asymptotic Tests. Journal of Econometrics, 2006, 133: 421-441.
[30] Davidson, R. and MacKinnon, J. G. Improving the Reliability of Bootstrap Tests with the Fast Double Bootstrap. Computational Statistics & Data Analysis, 2007, 51: 3259-3281.
[31] Dickey, D. A. and Fuller, W. A. Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 1979, 74: 427-431.
[32] Duffee, G. R. Stock Returns and Volatility: a Firm-Level Analysis. Journal of Finance Economics, 1995, 37: 399-420.
[33] Dufour, J.-M. and Khalaf, L. Simulation Based Finite and Large Sample Tests in Multivariate Regressions. Journal of Econometrics, 2002, 111(2): 303-322.
[34] Dwivedi, T. D. and Srivastava, V. K. Optimality of Least Squares in the Seemingly Unrelated Regressions Model. Journal of Econometrics, 1978, 7: 391-395.
[35] Feldstein, M. and Horioka, C. Domestic Saving and International Capital Flows. Economic Jouranl, 1980, 90: 314-329.
[36] Ford, G. S., Jackson, J. D. and Kline, A. D. Misleading Inferences from Panel Unit Root Tests: a Comment. Review of International Economics, 2006, 14(3): 508-511.
[37] Gengenbach, C., Palm, F. C. and Urbain, J.-P. Cointegration Testing in Panels with Common Factors. Oxford Bulletin of Economics and Statistics, 2006, Supplement, 68: 683-719.
[38] Geweke, J. Contemporary Bayesian Econometrics and Statistics. Hoboken, NJ: Wiley, 2003.
[39] Greene, W. H. Econometric Analysis, 5th Edith. New Jersey: Prentice Hall, 2003.
[40] Groen, J. J. J. and Kleibergen, F. Likelihood-Based Cointegration Analysis in Panels of Vector Error Correction Models. Journal of Business and Economic Statistics, 2003, 21: 295-318.
[41] Gundlach, E. and Sinn, S. Unit– Root Tests of the Current Account: Implication for International Capital Mobility. Applied Economics, 1992, 24: 617-625.
[42] Hadri, K. Testing for Unit Roots in Heterogeneous Panel Data. Econometrics Journal, 2000, 3:148-161.
[43] Hadri, K. and Larsson, R. Testing for Stationarity in Heterogeneous Panel Data where the Time Dimension Is Finite. Econometrics Journal, 2005, 8: 55-69.
[44] Harris, R. D. F. and Tzavalis, E. Inference for Unit Roots in Dynamic Panels where the Time Dimension is Fixed. Journal of Econometrics, 1999, 91: 201-226.
[45] Ho, T.-W. A Re-examination of the Unbiasedness Forward Rate Hypothesis Using Dynamic SUR Model. The Quarterly Review of Economics and Finance, 2003, 43: 542-559.
[46] Hodgson, D., Linton, O. and Vorkink, K. Testing the Capital Asset Pricing Model Efficiently under Elliptical Symmetry: A Semiparametric Approach. Journal of Applied Econometrics, 2002, 17: 619-639.
[47] Horowitz, J. The Bootstrap in Heckman, J. J. and Leamer, E. E. eds. Handbook of Econometrics. Elsevier Science B. V., 2001, Vol. 5: 3159-3228.
[48] Hurlin, C. and Mignon, V. Second Generation Panel Unit Root Tests. Mimeo, LEO, University of Orleans, 2004.
[49] Im, K. S., Pesaran, M. H. and Shin, Y. Testing for Unit Roots in Heterogeneous Panels. DAE, Working Paper 9526, University of Cambridge, 1997.
[50] Im, K. S., Pesaran, M. H. and Shin, Y. Testing for Unit Roots in Heterogeneous Panels. Journal of Econometrics, 2003, 115, 1: 53-74.
[51] Jerrett, M., Eyles, J., Dufournaud, C. and Birch, S. Environmental Influences on Health Care Expenditures: An Exploratory Analysis from Ontario, Canada. Journal of Epidemiology and Community Health, 2003, 57: 334-338.
[52] Johansen, S. Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control. 1988, 12: 231-254.
[53] Johansen, S. Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 1991, 59: 1551-1580.
[54] Judge, G. G., Griffiths, W. E., Hill, R. C. et al. The Theory and Practice of Econometrics, 2nd edition. New York: John Wiley and Sons, 1985.
[55] Kao, C. Spurious Regression and Residual-Based Tests for Cointegration in Panel Data. Journal of Econometrics, 1999, 90: 1-44.
[56] Kao, C. and Chiang, M. -H. On the Estimation and Inference of a Cointegrated Regression in Panel Data. Advances in Econometrics, 2000, 15: 179-222.
[57] Kim, H., Oh, K.-Y. and Jeong, C.-W. Panel Cointegration Results on International Capital Mobility in Asian Economies. Journal of International Money and Finance, 2005, 24: 71-82.
[58] Kwiatkowski, D., Phillips, P. C. B., Schmidt, P. and Shin, Y. Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root. Journal of Econometrics, 1992, 54: 91-115.
[59] Larsson, R., Lyhagen, J. and Lothgren, M. Likelihood-Based Cointegration Test in Heterogeneous Panels. Econometrics Journal, 2001, 4: 109-142.
[60] Levin, A. and Lin, C. F. Unit Root Tests in Panel Data: Asymptotic and Finite Sample Properties. Department of Economics, University of California at San Diego, Discussion Paper No. 92-93, 1992(revised 1993).
[61] Levin, A. and Lin, C. F. Unit Root Tests in Panel Data: New Results. Department of Economics, University of California at San Diego, Discussion Paper No. 93-56, 1993.
[62] Levin, A., Lin, C. F. and Chu, C. S. J. Unit Root Test in Panel Data: Asymptotic and Finite Sample Properties. Journal of Econometrics, 2002, 108: 1-24.
[63] Li, H. and Maddala, J. S. Bootstrapping Cointegrating Regressions. Journal of Econometrics, 1997, 80: 297-348.
[64] Maddala, G. S. and Wu, S. A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test. Oxford Bulletin of Economics and Statistics, 1999, special issue: 631-652.
[65] Mark, N.C., Ogaki, M. and Sul, D. Dynamic Seemingly Unrelated CointegratingRegressions. Review of Economic Studies, 2005, 72: 797-820.
[66] McCoskey, S. and Kao, C. A Residual-Based Test of the Null of Cointegration in Panel Data. Econometric Reviews, 1998, 17: 57-84.
[67] Mead, R.W. and Brajer, V. Protecting China’s Children: Valuing the Health Impacts of Reduced Air Pollution in Chinese Cities. Environment and Development Economics, 2005, 10: 745-768.
[68] Messemer, C. and Parks, R. W. Bootstrap Methods for Inference in a SUR model with Autocorrelated Disturbances. Mimeo, Lewis & Clark College Portland, Oregon, 2004.
[69] Moon, R. H. A Note on Fully-Modified Estimation of Seemingly Unrelated Regressions Models with Integrated Regressors. Economics Letters, 1999, 65: 25-31.
[70] Moon, R. H. and Perron, B. The Seemingly Unrelated Dynamic Cointegration Regression Model and Testing for Purchasing Power Parity. Mimeo, University of Southern California, 2000.
[71] Moon, R. H. and Perron, B. Asymptotic Local Power of Pooled t-Ratio Tests for Unit Roots in Panels with Fixed Effects. Mimeo, University of Montreal, 2004a.
[72] Moon, R. H. and Perron, B. Testing for a Unit Root in Panels with Dynamic Factors. Journal of Econometrics, 2004b, 122: 81-126.
[73] Moon, H. R. and Perron, B. Seemingly Unrelated Regressions. Mimeo, University of Southern California, 2006.
[74] Murphy, R. G. Capital Mobility and the Relationship between Saving and Investment in OECD Countries, Journal of International Money and Finance, 1984, 3: 327-342.
[75] Narayan, P. K. and Narayan, S. Does Environmental Quality Influence Health Expenditures? Empirical Evidence from A Panel of Selected OECD Countries. Ecological Economics, 2007, doi:10.1016/j.ecolecon.2007.07.005.
[76] Neidell, M. J. Air Pollution, Health and Socio-Economic Status: the Effect of Outdoor Air Quality on Childhood Asthma. Journal of Health Economics, 2004, 23: 1209-1236.
[77] Newhouse, J. P. Medical-Care Expenditure: A Cross-National Survey. Journal of Human Resources, 1977, 12: 115-125.
[78] Oberhofer, W. and Kmenta, J. A General Procedure for Obtaining Maximum Likelihood Estimates in Generalized Regression Models. Econometrica, 1974, 42: 579-590.
[79] Parks, R. Efficient Estimation of a System of Regression Equations When Disturbances Are Both Serially and Contemporaneously Correlated. Journal of the American Statistical Association, 1967, 62: 500-509.
[80] Park, J. Y. and Ogaki, M. Seemingly Unrelated Canonical Cointegrating Regressions. Working Paper, No. 280, Rochester Center for Economic Research, 1991.
[81] Pedroni, P. Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors. Oxford Bulletin of Economics and Statistics, 1999, 61: 653-670.
[82] Pedroni, P. Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis. Econometric Theory, 2004, 3: 579-625.
[83] Pesaran, H. M. A Simple Panel Unit Root Test in the Presence of Cross Section Dependence. Mimeo, University of Southern California, 2003.
[84] Phillips, P. C. B. The Exact Distribution of the SUR Estimator. Econometrica, 1985, 53: 745-756.
[85] Phillips, P. C. B. and Moon, H. R. Linear Regression Limit Theory for Nonstationary Panel Data. Econometrica, 1999, 67: 1057-1111.
[86] Phillips, P. C. B. and Sul, D. Dynamic Panel Estimation and Homogeneity Testing under Cross-Section Dependence. Econometrics Journal, 2003, 6: 217-259.
[87] Quah, D. Exploiting Cross-Section Variations for Unit Root Inference in Dynamic Data. Economics Letters, 1994, 44: 9-19.
[88] Saikkonen, P. Asymptotically Efficient Estimation of Cointegration Regressions. Econometric Theory, 1991, 7: 1-21.
[89] Schmidt, P. Estimation of Seemingly Unrelated Regressions with Unequal Numbersof Observations. Journal of Econometrics, 1977, 5: 365-377.
[90] Schwert, G. W. and Seguin, P. J. Heteroskedasticity in Stock Returns. Journal of Finance, 1990, 45: 1129-1155.
[91] Shin, D. W. and Kang, S. An Instrumental Variable Approach for Panel Unit Root Tests under Cross-Sectional Dependence. Journal of Econometrics, 2006, 134: 215-234.
[92] Srivastava, V. K. and Giles, D. E. A. Seemingly Unrelated Regression Equations Models: Estimation and Inference. New York: Marcel Dekker, 1987.
[93] Stock, J. H. and Watson, M. W. A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 1993, 61: 783-820.
[94] Strauss, J. and Yigit, T. Shortfalls of Panel Unit Root Testing. Economics Letters, 2003, 81: 309-313.
[95] Tabak, B. M. and Guerra, S. M. Stock Returns and Volatility. Mimeo, Banco Central Do Brasil, DEPEP, 2002.
[96] Taylor, M. P. and Sarno, L. The Behavior of Real Exchange Rates during the Post-Bretton Woods Period. Journal of International Economics, 1998, 46: 281-312.
[97] West, K. D. Asymptotic Normality when Regressors Have a Unit Root. Econometrica, 1988, 56: 1397-1418.
[98] Westerlund, J. Reducing the Size Distortions of the Panel LM Test for Cointegration. Economics Letters, 2006, 90: 384-389.
[99] Westerlund, J. and Edgerton, D. L. A Panel Bootstrap Cointegration Test. Economics Letters, 2007, 97: 185-190.
[100] Zellner, A. An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias. Journal of the American Statistical Association, 1962, 57: 348-368.
[101]高梦滔,姚洋.健康风险冲击对农户收入的影响.经济研究, 2005, 12: 15-25.
[102]胡永平,张宗益,祝接金.基于储蓄—投资关系的中国区域间资本流动分析.中国软科学, 2004, 5: 130-134.
[103]麦勇,李勇. 1982-2005年中国省域资本流动差异解析.中国软科学, 2006, 12: 79-86.
[104]欧阳志刚.农民医疗卫生支出影响因素的综列协整分析.世界经济, 2007, 9: 47-55.
[105]彭方平.我国股市的均值回复性与参数价值反转投资策略——基于上海股票市场的经验分析.管理工程学报, 2008, 22(1): 125-127.
[106]沈可挺,刘煜辉.中国股市中惯性与反向投资策略的获利模式.管理科学学报, 2006, 9(6): 43-52.
[107]宋桂香,江莉莉,陈国海等.上海市大气气态污染物与居民每日死亡关系的时间序列研究.环境与健康杂志, 2006, 23(5): 390-393.
[108]徐小华,何佳,吴冲锋.我国债券市场价格非对称性波动研究.金融研究, 2006, 12: 14-22.
[109]张路胶,赵华.中国股市波动率的非对称性.统计与决策, 2006, 10: 80-82.
[2] Avik, C. The Saving– Investment Relationship Revisited: New Evidence from Multivariate Heterogeneous Panel Cointegration Analyses. Journal of Comparative Economics, 2006, 34: 402-419.
[3] Bai, J. and Ng, S. A PANIC Attack on Unit Roots and Cointegration. Econometrica, 2004, 72: 1127-1177.
[4] Baltagi, B. H. Econometrics, 4th Edition. New York: Springer– Verlag, 2008.
[5] Banerjee, A., Marcellino, M. and Osbat, C. Some Cautions on the Use of Panel Methods for Integrated Series of Macro-Economic Data. Econometrics Journal, 2004, 7: 322-340.
[6] Banister, J. Population, Public Health and the Environment in China. The China Quarterly, 1998, 156: 986-1015.
[7] Bartels, R. and Feibig, D. G. A Simple Characterization of Seemingly Unrelated Regressions Models in Which OLS is BLUE. American Statistician, 1991, 45: 137-140.
[8] Bayoumi, T. Saving– Investment Correlations: Immobile Capital, Government Policy or Endogenous Behavior. IMF Staff Paper, 1990, 37: 360-387.
[9] Beran, R. Prepivoting Test Statistics: a Bootstrap View of Asymptotic Refinements. Journal of the American Statistical Association, 1988, 83: 687-697.
[10] Berenguer-Rico, V. and Carrion-I-Silvestre, J. L. Testing for Multicointegration in Panel Data with Common Factors. Oxford Bulletin of Economics and Statistics, 2006, Supplement, 68: 721-739.
[11] Berndt, E. R. The Practice of Econometrics: Classic and Contemporary. Addison– Wesley: Reading, MA, 1991.
[12] Bornhorst, F. and Baum, C. F. IPSHIN: Stata Module to Perform Im-Pesaran-Shin Panel Unit Root Test. Boston College, Department of Economics, Statistical Software Component, No.S419704, 2001.
[13] Breitung, J. and Das, S. Panel Unit Root Tests under Cross-Sectional Dependence. Statistica Neerlandica, 2005, 59(4): 414-433.
[14] Breuer, J. B., McNown, R. and Wallace, M. S. Misleading Inferences from Panel Unit-Root Tests with an Illustration from Purchasing Power Parity. Review of International Economics, 2001, 9(3): 482-493.
[15] Breuer, J. B., McNown, R. and Wallace, M. S. Misleading Inferences from Panel Unit Root Tests: a Reply. Review of International Economics, 2006, 14(3): 512-516.
[16] Breusch, T. S. and Pagan, A. R. The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics. Review of Economic Studies, 1980, 47: 239-253.
[17] Cerrato, M. and Sarantis, N. A Bootstrap Panel Unit Root Test under Cross-Sectional Dependence, with an Application to PPP. Computational Statistics & Data Analysis, 2007, 51: 4028-4037.
[18] Chang, Y. Nonlinear IV Unit Root Tests in Panels with Cross-Sectional Dependency. Journal of Econometrics, 2002, 110: 261-292.
[19] Chang, Y. Bootstrap Unit Root Tests in Panels with Cross-Sectional Dependency. Journal of Econometrics, 2004, 120: 263-293.
[20] Chang, Y., Park, J. Y. and Song, K. Bootstrapping Cointegrating Regressions. Journal of Econometrics, 2006, 133: 703-739.
[21] Cheung, Y.-W. and Ng, L. Stock Price Dynamics and Firm Size: an Empirical Investigation. Journal of Finance, 1992, 47: 1985-1997.
[22] Choi, I. Unit Root Tests for Panel Data. Journal of International Money and Finance, 2001, 20: 249-272.
[23] Choi, I. Combination Unit Root Tests for Cross-Sectionally Correlated Panels. Mimeo, Hong Kong University of Science and Technology, 2002.
[24] Christie, A. The Stochastic Behavior of Common Stock Variances: Value, Leverage, and Interest Rate Effects. Journal of Financial Economics, 1982, 10: 407-432.
[25] Coakley, J., Kulasi, F. and Smith, R. Current Account Solvency and the Feldstein-Horioka Puzzle. Economic Journal, 1996, 106: 620-627.
[26] Coakley, J., Kulasi, F. and Smith, R. The Feldstein-Horioka Puzzle and CapitalMobility: a Review. International Journal of Finance and Economics, 1998, 3: 169-188.
[27] Davidson, R. and MacKinnon, J. G. The Size Distortion of Bootstrap Tests. Econometric Theory, 1999, 15: 361-376.
[28] Davidson, R. and MacKinnon, J. G. Econometric Theory and Methods. New York: Oxford University Press, 2004.
[29] Davidson, R. and MacKinnon, J. G. The Power of Bootstrap and Asymptotic Tests. Journal of Econometrics, 2006, 133: 421-441.
[30] Davidson, R. and MacKinnon, J. G. Improving the Reliability of Bootstrap Tests with the Fast Double Bootstrap. Computational Statistics & Data Analysis, 2007, 51: 3259-3281.
[31] Dickey, D. A. and Fuller, W. A. Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 1979, 74: 427-431.
[32] Duffee, G. R. Stock Returns and Volatility: a Firm-Level Analysis. Journal of Finance Economics, 1995, 37: 399-420.
[33] Dufour, J.-M. and Khalaf, L. Simulation Based Finite and Large Sample Tests in Multivariate Regressions. Journal of Econometrics, 2002, 111(2): 303-322.
[34] Dwivedi, T. D. and Srivastava, V. K. Optimality of Least Squares in the Seemingly Unrelated Regressions Model. Journal of Econometrics, 1978, 7: 391-395.
[35] Feldstein, M. and Horioka, C. Domestic Saving and International Capital Flows. Economic Jouranl, 1980, 90: 314-329.
[36] Ford, G. S., Jackson, J. D. and Kline, A. D. Misleading Inferences from Panel Unit Root Tests: a Comment. Review of International Economics, 2006, 14(3): 508-511.
[37] Gengenbach, C., Palm, F. C. and Urbain, J.-P. Cointegration Testing in Panels with Common Factors. Oxford Bulletin of Economics and Statistics, 2006, Supplement, 68: 683-719.
[38] Geweke, J. Contemporary Bayesian Econometrics and Statistics. Hoboken, NJ: Wiley, 2003.
[39] Greene, W. H. Econometric Analysis, 5th Edith. New Jersey: Prentice Hall, 2003.
[40] Groen, J. J. J. and Kleibergen, F. Likelihood-Based Cointegration Analysis in Panels of Vector Error Correction Models. Journal of Business and Economic Statistics, 2003, 21: 295-318.
[41] Gundlach, E. and Sinn, S. Unit– Root Tests of the Current Account: Implication for International Capital Mobility. Applied Economics, 1992, 24: 617-625.
[42] Hadri, K. Testing for Unit Roots in Heterogeneous Panel Data. Econometrics Journal, 2000, 3:148-161.
[43] Hadri, K. and Larsson, R. Testing for Stationarity in Heterogeneous Panel Data where the Time Dimension Is Finite. Econometrics Journal, 2005, 8: 55-69.
[44] Harris, R. D. F. and Tzavalis, E. Inference for Unit Roots in Dynamic Panels where the Time Dimension is Fixed. Journal of Econometrics, 1999, 91: 201-226.
[45] Ho, T.-W. A Re-examination of the Unbiasedness Forward Rate Hypothesis Using Dynamic SUR Model. The Quarterly Review of Economics and Finance, 2003, 43: 542-559.
[46] Hodgson, D., Linton, O. and Vorkink, K. Testing the Capital Asset Pricing Model Efficiently under Elliptical Symmetry: A Semiparametric Approach. Journal of Applied Econometrics, 2002, 17: 619-639.
[47] Horowitz, J. The Bootstrap in Heckman, J. J. and Leamer, E. E. eds. Handbook of Econometrics. Elsevier Science B. V., 2001, Vol. 5: 3159-3228.
[48] Hurlin, C. and Mignon, V. Second Generation Panel Unit Root Tests. Mimeo, LEO, University of Orleans, 2004.
[49] Im, K. S., Pesaran, M. H. and Shin, Y. Testing for Unit Roots in Heterogeneous Panels. DAE, Working Paper 9526, University of Cambridge, 1997.
[50] Im, K. S., Pesaran, M. H. and Shin, Y. Testing for Unit Roots in Heterogeneous Panels. Journal of Econometrics, 2003, 115, 1: 53-74.
[51] Jerrett, M., Eyles, J., Dufournaud, C. and Birch, S. Environmental Influences on Health Care Expenditures: An Exploratory Analysis from Ontario, Canada. Journal of Epidemiology and Community Health, 2003, 57: 334-338.
[52] Johansen, S. Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control. 1988, 12: 231-254.
[53] Johansen, S. Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 1991, 59: 1551-1580.
[54] Judge, G. G., Griffiths, W. E., Hill, R. C. et al. The Theory and Practice of Econometrics, 2nd edition. New York: John Wiley and Sons, 1985.
[55] Kao, C. Spurious Regression and Residual-Based Tests for Cointegration in Panel Data. Journal of Econometrics, 1999, 90: 1-44.
[56] Kao, C. and Chiang, M. -H. On the Estimation and Inference of a Cointegrated Regression in Panel Data. Advances in Econometrics, 2000, 15: 179-222.
[57] Kim, H., Oh, K.-Y. and Jeong, C.-W. Panel Cointegration Results on International Capital Mobility in Asian Economies. Journal of International Money and Finance, 2005, 24: 71-82.
[58] Kwiatkowski, D., Phillips, P. C. B., Schmidt, P. and Shin, Y. Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root. Journal of Econometrics, 1992, 54: 91-115.
[59] Larsson, R., Lyhagen, J. and Lothgren, M. Likelihood-Based Cointegration Test in Heterogeneous Panels. Econometrics Journal, 2001, 4: 109-142.
[60] Levin, A. and Lin, C. F. Unit Root Tests in Panel Data: Asymptotic and Finite Sample Properties. Department of Economics, University of California at San Diego, Discussion Paper No. 92-93, 1992(revised 1993).
[61] Levin, A. and Lin, C. F. Unit Root Tests in Panel Data: New Results. Department of Economics, University of California at San Diego, Discussion Paper No. 93-56, 1993.
[62] Levin, A., Lin, C. F. and Chu, C. S. J. Unit Root Test in Panel Data: Asymptotic and Finite Sample Properties. Journal of Econometrics, 2002, 108: 1-24.
[63] Li, H. and Maddala, J. S. Bootstrapping Cointegrating Regressions. Journal of Econometrics, 1997, 80: 297-348.
[64] Maddala, G. S. and Wu, S. A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test. Oxford Bulletin of Economics and Statistics, 1999, special issue: 631-652.
[65] Mark, N.C., Ogaki, M. and Sul, D. Dynamic Seemingly Unrelated CointegratingRegressions. Review of Economic Studies, 2005, 72: 797-820.
[66] McCoskey, S. and Kao, C. A Residual-Based Test of the Null of Cointegration in Panel Data. Econometric Reviews, 1998, 17: 57-84.
[67] Mead, R.W. and Brajer, V. Protecting China’s Children: Valuing the Health Impacts of Reduced Air Pollution in Chinese Cities. Environment and Development Economics, 2005, 10: 745-768.
[68] Messemer, C. and Parks, R. W. Bootstrap Methods for Inference in a SUR model with Autocorrelated Disturbances. Mimeo, Lewis & Clark College Portland, Oregon, 2004.
[69] Moon, R. H. A Note on Fully-Modified Estimation of Seemingly Unrelated Regressions Models with Integrated Regressors. Economics Letters, 1999, 65: 25-31.
[70] Moon, R. H. and Perron, B. The Seemingly Unrelated Dynamic Cointegration Regression Model and Testing for Purchasing Power Parity. Mimeo, University of Southern California, 2000.
[71] Moon, R. H. and Perron, B. Asymptotic Local Power of Pooled t-Ratio Tests for Unit Roots in Panels with Fixed Effects. Mimeo, University of Montreal, 2004a.
[72] Moon, R. H. and Perron, B. Testing for a Unit Root in Panels with Dynamic Factors. Journal of Econometrics, 2004b, 122: 81-126.
[73] Moon, H. R. and Perron, B. Seemingly Unrelated Regressions. Mimeo, University of Southern California, 2006.
[74] Murphy, R. G. Capital Mobility and the Relationship between Saving and Investment in OECD Countries, Journal of International Money and Finance, 1984, 3: 327-342.
[75] Narayan, P. K. and Narayan, S. Does Environmental Quality Influence Health Expenditures? Empirical Evidence from A Panel of Selected OECD Countries. Ecological Economics, 2007, doi:10.1016/j.ecolecon.2007.07.005.
[76] Neidell, M. J. Air Pollution, Health and Socio-Economic Status: the Effect of Outdoor Air Quality on Childhood Asthma. Journal of Health Economics, 2004, 23: 1209-1236.
[77] Newhouse, J. P. Medical-Care Expenditure: A Cross-National Survey. Journal of Human Resources, 1977, 12: 115-125.
[78] Oberhofer, W. and Kmenta, J. A General Procedure for Obtaining Maximum Likelihood Estimates in Generalized Regression Models. Econometrica, 1974, 42: 579-590.
[79] Parks, R. Efficient Estimation of a System of Regression Equations When Disturbances Are Both Serially and Contemporaneously Correlated. Journal of the American Statistical Association, 1967, 62: 500-509.
[80] Park, J. Y. and Ogaki, M. Seemingly Unrelated Canonical Cointegrating Regressions. Working Paper, No. 280, Rochester Center for Economic Research, 1991.
[81] Pedroni, P. Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors. Oxford Bulletin of Economics and Statistics, 1999, 61: 653-670.
[82] Pedroni, P. Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis. Econometric Theory, 2004, 3: 579-625.
[83] Pesaran, H. M. A Simple Panel Unit Root Test in the Presence of Cross Section Dependence. Mimeo, University of Southern California, 2003.
[84] Phillips, P. C. B. The Exact Distribution of the SUR Estimator. Econometrica, 1985, 53: 745-756.
[85] Phillips, P. C. B. and Moon, H. R. Linear Regression Limit Theory for Nonstationary Panel Data. Econometrica, 1999, 67: 1057-1111.
[86] Phillips, P. C. B. and Sul, D. Dynamic Panel Estimation and Homogeneity Testing under Cross-Section Dependence. Econometrics Journal, 2003, 6: 217-259.
[87] Quah, D. Exploiting Cross-Section Variations for Unit Root Inference in Dynamic Data. Economics Letters, 1994, 44: 9-19.
[88] Saikkonen, P. Asymptotically Efficient Estimation of Cointegration Regressions. Econometric Theory, 1991, 7: 1-21.
[89] Schmidt, P. Estimation of Seemingly Unrelated Regressions with Unequal Numbersof Observations. Journal of Econometrics, 1977, 5: 365-377.
[90] Schwert, G. W. and Seguin, P. J. Heteroskedasticity in Stock Returns. Journal of Finance, 1990, 45: 1129-1155.
[91] Shin, D. W. and Kang, S. An Instrumental Variable Approach for Panel Unit Root Tests under Cross-Sectional Dependence. Journal of Econometrics, 2006, 134: 215-234.
[92] Srivastava, V. K. and Giles, D. E. A. Seemingly Unrelated Regression Equations Models: Estimation and Inference. New York: Marcel Dekker, 1987.
[93] Stock, J. H. and Watson, M. W. A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 1993, 61: 783-820.
[94] Strauss, J. and Yigit, T. Shortfalls of Panel Unit Root Testing. Economics Letters, 2003, 81: 309-313.
[95] Tabak, B. M. and Guerra, S. M. Stock Returns and Volatility. Mimeo, Banco Central Do Brasil, DEPEP, 2002.
[96] Taylor, M. P. and Sarno, L. The Behavior of Real Exchange Rates during the Post-Bretton Woods Period. Journal of International Economics, 1998, 46: 281-312.
[97] West, K. D. Asymptotic Normality when Regressors Have a Unit Root. Econometrica, 1988, 56: 1397-1418.
[98] Westerlund, J. Reducing the Size Distortions of the Panel LM Test for Cointegration. Economics Letters, 2006, 90: 384-389.
[99] Westerlund, J. and Edgerton, D. L. A Panel Bootstrap Cointegration Test. Economics Letters, 2007, 97: 185-190.
[100] Zellner, A. An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias. Journal of the American Statistical Association, 1962, 57: 348-368.
[101]高梦滔,姚洋.健康风险冲击对农户收入的影响.经济研究, 2005, 12: 15-25.
[102]胡永平,张宗益,祝接金.基于储蓄—投资关系的中国区域间资本流动分析.中国软科学, 2004, 5: 130-134.
[103]麦勇,李勇. 1982-2005年中国省域资本流动差异解析.中国软科学, 2006, 12: 79-86.
[104]欧阳志刚.农民医疗卫生支出影响因素的综列协整分析.世界经济, 2007, 9: 47-55.
[105]彭方平.我国股市的均值回复性与参数价值反转投资策略——基于上海股票市场的经验分析.管理工程学报, 2008, 22(1): 125-127.
[106]沈可挺,刘煜辉.中国股市中惯性与反向投资策略的获利模式.管理科学学报, 2006, 9(6): 43-52.
[107]宋桂香,江莉莉,陈国海等.上海市大气气态污染物与居民每日死亡关系的时间序列研究.环境与健康杂志, 2006, 23(5): 390-393.
[108]徐小华,何佳,吴冲锋.我国债券市场价格非对称性波动研究.金融研究, 2006, 12: 14-22.
[109]张路胶,赵华.中国股市波动率的非对称性.统计与决策, 2006, 10: 80-82.