中国卫生总费用影响因素与预测方法学研究
|
摘要
目的：运用科学、合理的方法找出影响我国卫生总费用增长的主要因素,探讨卫生总费用与其影响因素间的长期均衡及短期波动关系。采用三种预测方法对我国卫生总费用未来发展趋势进行预测,并对拟合及其预测效果进行综合评价。为卫生经济政策的制定,卫生资源的有效利用以及卫生总费用的预测研究提供参考依据。
方法：基于协整理论、误差修正模型以及格兰杰因果检验,选取以需方因素(收入、卫生事业费、人口老龄化、城市化水平)和供方因素(医师人数、医院床位数)为自变量,卫生总费用为因变量建立卫生总费用影响因素动态回归模型,探讨我国卫生总费用与其影响因素间的长期均衡与短期波动关系,并对变量间的因果关系进行验证。通过对数转换以及差分的方式实现研究序列的方差齐性和平稳化,运用自回归移动平均模型、残差自回归模型以及状态空间模型对我国1978-2005年卫生总费用数据进行模型拟合,并引入均方误差、平均绝对百分误差以及均方根误差3个统计量利用2006-2008年卫生总费用数据进行模型预测效果的检验,最后,在此基础上,进一步对我国2009-2020年卫生总费用的发展趋势进行预测。所有统计资料及数据采用Excel软件建立数据库,运用SAS9.13进行统计分析及预测研究,统计假设检验采用双侧检验,检验水准α=0.05。
结果：①卫生总费用影响因素回归模型分析研究发现,卫生总费用与收入(GDP)、卫生事业费、人口老龄化、城市化水平以及医院床位数之间存在长期均衡关系；人口老龄化是卫生总费用短期波动的主要因素,其次为收入和医院床位数。其中,卫生总费用相对于收入的长期弹性系数为2.110,短期弹性系数为0.581。这说明1978-2008年,我国GDP每增长1个百分点,卫生总费用相应的增长2.110个百分点,卫生总费用的增长速度快于经济增长速度；而本年度GDP每增长1个百分点,将引起本年度卫生总费用增长0.581个百分点。Granger因果关系检验结果显示：卫生总费用是经济增长和医院床位数增加的原因,而人口老龄化和经济增长是卫生总费用增长的原因。卫生总费用未来发展趋势研究结果发现,至2020年人均实际卫生费用三种模型的预测值分别为691.536元、720.130元和944.466元,其中状态空间模型人均实际卫生费用预测值显著高于其他两种模型预测结果；残差自回归模型人均实际卫生费用预测值略高于自回归移动平均模型。
②三种预测方法对比分析结果显示状态空间模型的拟合精度在三个评价指标上是最优的,自回归残差模型次之,但ARIMA模型拟合的平均绝对百分误差最小；在模型预测精度方面,状态空间模型及自回归误差预测精度远远优于ARIMA模型,其中,自回归误差模型样本外预测的均方误差较小,而状态空间模型样本外预测的平均绝对百分误差较小。
结论：①收入(GDP)、卫生事业费、人口老龄化、城市化水平以及医院床位数是卫生总费用长期变动的主要因素,且人口老龄化对我国卫生总费用的影响己逐渐占主导地位。但卫生事业费、城市化水平对卫生总费用的短期波动影响不大。
②卫生总费用是引起经济增长以及医院床位数变化的原因。而入口老龄化和经济增长也是卫生总费用增长的原因。
③状态空间模型可以应用于我国卫生总费用时间序列预测研究中,能够将影响因素纳入研究范围,并消除由于外界冲击、政策变化等不可测因素的影响。在卫生总费用预测研究中具有重要的实际意义。
Objectives:
To find the main determinants of Total Health Expenditures in China using scientific and reasonable methods, and to discuss the long-term equilibrium and short-term fluctuation relationship between Total Health Expenditures and the determinants, and to forecast the trends of China's total health expenditure in future with three kinds of prediction methods, and as well as to assess synthetically the precision of those methods. At same time, to provide scientific evidence for health economic policy and the efficient use of resources, as well as the total health expenditure forecast study.
Methods:
Dynamic regression model was established between Total Health Expenditures and independent variables with the methods of co-integration approach, error correction model and granger causality test, the independent variables included demand factors (income, health expenditures, Population aging, urbanization) and supply factors (The number of physicians and hospital beds). Logarithmic transformation and difference were brought to achieve the homogeneity of variance and stationary of sequence. Trough auto-regression integrated moving average, ARIMA model, and auto-regression model and state space model to fitting the time series data of Total Health Expenditures in China from 1978 to 2005. three indicators such as Mean squared error and Mean absolute percentage error and Root mean squared error were introduced to assess synthetically the precision of this three prediction methods based the time series data of China from 2006 to 2008. After that, making prediction to the total health expenditures trends in 2009-2020 in advance. The dataset was developed with the Excel software. All analyses and predictions were performed using SAS version 9.13. All of the statistical tests were two sided, and the 'significance level' wasα=0.05.
Results:
①The results from regression model for determinants and trends of the total health expenditures showed that there were long-term equilibrium relationship between the total health expenditures and determinants such as income, health expenditures, population aging, urbanization, the number of physicians and hospital beds, and the short-term fluctuation relationship existed in the total health expenditures and income, and population aging, and the number of hospital beds. However, health expenditure, urbanization and the number of physicians have no effect on the total health expenditures in short-term. The findings showed that the long-term elasticity was 2.110, and the short term elasticity was 0.581. The granger causality test illustrated that the total health expenditures were causality factors of the changes in income, and the number of hospital beds. Meanwhile, the incomes and the population aging were causality factors of the changes in total health expenditures. The three kinds of predictive models demonstrated per capita health expenditure would reach 691.536 Yuan,720.130 Yuan, and 944.466 Yuan in the year 2020, respectively. The predictive value of the state space model was the highest. The second was auto-regression model.
②The results of comparative Analysis of three predictive methods that the fitting accuracy of the state space model was the best. So was the auto-regression model. However, ARIMA model has the lower value in Mean absolute percentage error. On the other hand, the sate space model and the auto-regression model have an excellent prediction Accuracy. Auto-regression model had the lowest Mean squared error as well as the state space model had the lowest Mean absolute percentage error.
Conclusion:
①Income, health expenditures, population aging, urbanization, and the number of physicians and hospital beds were the determinants of the total health expenditures in long-term. And the impact of aging population has becoming dominant gradually. But, health expenditures, urbanization, and the number of physicians have no impact on the total health expenditures in short-term.
②The total health expenditures were causality factors of the changes in income and the number of hospital beds. Meanwhile, the income and the population aging were causality factors of the changes in total health expenditures too.
③Through adding the influencing factors in the study, and to eliminate the external shock, policy changes and other unpredictable factors, State space model can be applied to the time series prediction research of the total health expenditures. It will make the prediction of the total health expenditures has important practical significance.
方法：基于协整理论、误差修正模型以及格兰杰因果检验,选取以需方因素(收入、卫生事业费、人口老龄化、城市化水平)和供方因素(医师人数、医院床位数)为自变量,卫生总费用为因变量建立卫生总费用影响因素动态回归模型,探讨我国卫生总费用与其影响因素间的长期均衡与短期波动关系,并对变量间的因果关系进行验证。通过对数转换以及差分的方式实现研究序列的方差齐性和平稳化,运用自回归移动平均模型、残差自回归模型以及状态空间模型对我国1978-2005年卫生总费用数据进行模型拟合,并引入均方误差、平均绝对百分误差以及均方根误差3个统计量利用2006-2008年卫生总费用数据进行模型预测效果的检验,最后,在此基础上,进一步对我国2009-2020年卫生总费用的发展趋势进行预测。所有统计资料及数据采用Excel软件建立数据库,运用SAS9.13进行统计分析及预测研究,统计假设检验采用双侧检验,检验水准α=0.05。
结果：①卫生总费用影响因素回归模型分析研究发现,卫生总费用与收入(GDP)、卫生事业费、人口老龄化、城市化水平以及医院床位数之间存在长期均衡关系；人口老龄化是卫生总费用短期波动的主要因素,其次为收入和医院床位数。其中,卫生总费用相对于收入的长期弹性系数为2.110,短期弹性系数为0.581。这说明1978-2008年,我国GDP每增长1个百分点,卫生总费用相应的增长2.110个百分点,卫生总费用的增长速度快于经济增长速度；而本年度GDP每增长1个百分点,将引起本年度卫生总费用增长0.581个百分点。Granger因果关系检验结果显示：卫生总费用是经济增长和医院床位数增加的原因,而人口老龄化和经济增长是卫生总费用增长的原因。卫生总费用未来发展趋势研究结果发现,至2020年人均实际卫生费用三种模型的预测值分别为691.536元、720.130元和944.466元,其中状态空间模型人均实际卫生费用预测值显著高于其他两种模型预测结果；残差自回归模型人均实际卫生费用预测值略高于自回归移动平均模型。
②三种预测方法对比分析结果显示状态空间模型的拟合精度在三个评价指标上是最优的,自回归残差模型次之,但ARIMA模型拟合的平均绝对百分误差最小；在模型预测精度方面,状态空间模型及自回归误差预测精度远远优于ARIMA模型,其中,自回归误差模型样本外预测的均方误差较小,而状态空间模型样本外预测的平均绝对百分误差较小。
结论：①收入(GDP)、卫生事业费、人口老龄化、城市化水平以及医院床位数是卫生总费用长期变动的主要因素,且人口老龄化对我国卫生总费用的影响己逐渐占主导地位。但卫生事业费、城市化水平对卫生总费用的短期波动影响不大。
②卫生总费用是引起经济增长以及医院床位数变化的原因。而入口老龄化和经济增长也是卫生总费用增长的原因。
③状态空间模型可以应用于我国卫生总费用时间序列预测研究中,能够将影响因素纳入研究范围,并消除由于外界冲击、政策变化等不可测因素的影响。在卫生总费用预测研究中具有重要的实际意义。
Objectives:
To find the main determinants of Total Health Expenditures in China using scientific and reasonable methods, and to discuss the long-term equilibrium and short-term fluctuation relationship between Total Health Expenditures and the determinants, and to forecast the trends of China's total health expenditure in future with three kinds of prediction methods, and as well as to assess synthetically the precision of those methods. At same time, to provide scientific evidence for health economic policy and the efficient use of resources, as well as the total health expenditure forecast study.
Methods:
Dynamic regression model was established between Total Health Expenditures and independent variables with the methods of co-integration approach, error correction model and granger causality test, the independent variables included demand factors (income, health expenditures, Population aging, urbanization) and supply factors (The number of physicians and hospital beds). Logarithmic transformation and difference were brought to achieve the homogeneity of variance and stationary of sequence. Trough auto-regression integrated moving average, ARIMA model, and auto-regression model and state space model to fitting the time series data of Total Health Expenditures in China from 1978 to 2005. three indicators such as Mean squared error and Mean absolute percentage error and Root mean squared error were introduced to assess synthetically the precision of this three prediction methods based the time series data of China from 2006 to 2008. After that, making prediction to the total health expenditures trends in 2009-2020 in advance. The dataset was developed with the Excel software. All analyses and predictions were performed using SAS version 9.13. All of the statistical tests were two sided, and the 'significance level' wasα=0.05.
Results:
①The results from regression model for determinants and trends of the total health expenditures showed that there were long-term equilibrium relationship between the total health expenditures and determinants such as income, health expenditures, population aging, urbanization, the number of physicians and hospital beds, and the short-term fluctuation relationship existed in the total health expenditures and income, and population aging, and the number of hospital beds. However, health expenditure, urbanization and the number of physicians have no effect on the total health expenditures in short-term. The findings showed that the long-term elasticity was 2.110, and the short term elasticity was 0.581. The granger causality test illustrated that the total health expenditures were causality factors of the changes in income, and the number of hospital beds. Meanwhile, the incomes and the population aging were causality factors of the changes in total health expenditures. The three kinds of predictive models demonstrated per capita health expenditure would reach 691.536 Yuan,720.130 Yuan, and 944.466 Yuan in the year 2020, respectively. The predictive value of the state space model was the highest. The second was auto-regression model.
②The results of comparative Analysis of three predictive methods that the fitting accuracy of the state space model was the best. So was the auto-regression model. However, ARIMA model has the lower value in Mean absolute percentage error. On the other hand, the sate space model and the auto-regression model have an excellent prediction Accuracy. Auto-regression model had the lowest Mean squared error as well as the state space model had the lowest Mean absolute percentage error.
Conclusion:
①Income, health expenditures, population aging, urbanization, and the number of physicians and hospital beds were the determinants of the total health expenditures in long-term. And the impact of aging population has becoming dominant gradually. But, health expenditures, urbanization, and the number of physicians have no impact on the total health expenditures in short-term.
②The total health expenditures were causality factors of the changes in income and the number of hospital beds. Meanwhile, the income and the population aging were causality factors of the changes in total health expenditures too.
③Through adding the influencing factors in the study, and to eliminate the external shock, policy changes and other unpredictable factors, State space model can be applied to the time series prediction research of the total health expenditures. It will make the prediction of the total health expenditures has important practical significance.
引文
[1]张振忠,王禄生,杨洪伟.中国卫生费用核算研究报告[M].北京：人民卫生出版社,2009.8.
[2]卫生部卫生经济研究所.2008 年中国卫生总费用研究报告[R].北京：2008：100-102.
[3]Joseph P. Newhouse. Medical-Care Expenditure: A cross-national Survey [J].The Journal of Human Resources.1977,12(1):115-125.
[4]Leu R E. The Public-Private mix and international health care costs [J]. Journal of Health Economics.1986,79(2):251-277.
[5]Gerdtham U G, Jonsson B. An econometric analysis of health care expenditures, A cross section study of OECD countries [J]. Journal of Health Economics, 1922,11(1):63-68.
[6]Hitiris T, Posnet J. The determinants and the effects of health expenditure in developed countries [J]. Journal of Health Economics,1992,11(2):173-181.
[7]Francia L, Gitto L, Mennini F, Polistena B. An econometric analysis of OECD Countries health expenditure. iHEA 2007 6th World Congress:Explorations in Health Economics Paper[R]. Social Science Research Network. http://papers.ssm.com
[8]Granger, C.W.J and Newbold, P. Spurious regressions in econometrics, Journal of Econometrics [J],1974,(2):111-120.
[9]Granger, C.W.J and Engle, R.F. Application of spectral analysis in econometrics in D.R. Brillinger and P. R. Kishnaiah (eds) Handbook of Statistics, Amsterdam [J],1984,(5), Elsevier.
[10]Hansen P, King A. The determinants of health care expenditure:A co integration approach[J]. Journal of Health Economics [J].1996,(15):127-137.
[11]Blomqvist, A.G, Carter, R.A. Is health care really a luxury? [J]. Journal of Health Economics.16(2):207-229.
[12]McCoskey S.K, Selden T.M. Health care expenditures and GDP:Panel data unit root test results [J]. Journal of Health Economics.1998,17(3):369-376.
[13]Jewell T, Lee J, Tieslau M, Strazicich M.C. Stationarity, health expenditures and GDP:Evidence from panel unit root tests with heterogeneous structural breaks [J].Journal of Health Economics.2003,17(3):313-323.
[14]Im K.S., Lee J. Tieslau M, Panel LM unit root tests with level shifts [J]. Oxford Bulletin of Economics & Statistics.2005,67(3):393-419.
[15]Okunade A.,Karakus M. C. Unit root and cointegration tests: Timeseries versus panel estimates for international health expenditure models [J]. 2001,33(9):1131-1137.
[16]Dreger C, Reimers H.E. Health care expenditures in OECD Countries:A panel unit root and co integration analysis. Social Science Research Network, IZA Discussion Paper No.1469,2005.
[17]Pedroni, Perer. Critical values for co integration tests in heterogeneous panels with multiple repressors [J]. Oxford Bulletin of Economics and Statistics. 1999,61, Special Issue,653-670.
[18]Kao, Chiwa, McCoskey, Suzanne. A residual-based test of the null of co integration in panel data[J].Econometric Reviews.1998,17:57-84.
[19]Murthy N.R.V, Ukpolo V. Aggregate health care expenditures in the United Stated: Evidence from co integration tests [J]. Applied Economics,1994,26(8):797-802.
[20]Das A, Martin F. An econometric analysis of the US health care expenditure [J]. Global Journal of the Health Science [J].2010,2(1):150-159.
[21]Fuches V R. Forecasting health expenditures:Short, medium and long term [J]. Journal of Healthcare Finance,1998,26(3):56-72.
[22]Richard B, Saltman,et.A methodological note on combining health and social care expenditures into a single statistic for policy-making purposes [J].European Journal of Public Health.2001,11(1):93-96.
[23]何平平.中国卫生总费用增长因素研究[J].统计与信息论坛.2006,21(1)：37-41.
[24]何平,孟庆跃.基于误差修正模型的卫生总费用与GDP关系研究[J].中国卫生经济.2005,24(9)：20-22.
[25]候文,荏苒.中国卫生总费用与GDP协整和引导关系的实证分析[J].辽宁师范大学学报(自然科学版).2008,31(1)：12-14.
[26]刘柏,赵振全,汪玲丽等.我国THE和GDP之间影响关系的2个区间实证检验[J].中国卫生经济.2006,25(1)：36-39.
[27]雷海潮.中国卫生总费用占国内生产总值比例的预测研究[J].中国卫生经济,1998,1(8)：5-8.
[28]李亚青.应用SimFin模型预测卫生总费用占GDP比值的变化趋势[J].中国卫生资源.2002,5(1)：29-30.
[29]王燕.应用时间序列分析[M].北京：中国人民大学出版社.2005：209-230.
[30]易丹辉.数据分析与Eviews应用[M].北京：中国统计出版社,2002：150.
[31]庞皓,李南成.计量经济学[M].四川：西南财经大学出版社,2005：239-242.
[32]Granger, C. W. J. Investigating Causal Relations by Econometric Models and Cross-spectral Methods[J]. Econometrica,37(3):424-438.
[33]邹平.金融计量学[M].上海：上海财经大学出版社有限公司,2010：174-175.
[34]Bovas, A, Johannes, L. Statistical methods for forecasting [M]. New Jersey: John Wiley & Sons, Inc.2005:2-3.
[35]Montgomery, D.C., Jennings, C.L., Introduction to time series analysis and forecasting [M]. Hoboken, NJ,2008:34.
[36]Fox, J. Applied regression analysis and generalized linear model (2nd ed.)[M]. los Angeles,2008.
[37]Spanos, A. Statistical foundations of econometric modeling [M]. New York: Cambridge University Press.
[38]West, M., & Harrison, J. Bayesian forecasting and dynamic models (2nd ed.)[M].New York: Springer.
[39]高慧璇等编译.SAS系统SAS/ETS软件使用手册[M].北京：中国统计出版社,1998：481-482.
[40]Andrew. H, Siem. J.K, Neil. S. State space and unobserved component models [M]. New York:Cambridge University Press.2004:5-11.
[41]赫尔穆特.鲁克波尔(德),马库斯.克莱茨希(德)编,易行健,邓可斌等译.应用时间序列计量经济学[M].北京：机械工业出版社,2008：68-70.
[42]Rob J. Hyndman, Anne B. Koehler, J. Keith Ord. Forecasting with exponential smoothing-the state space approach [M]. Berlin: Springer:2008:25-27.
[43]薛勇,郭菊娥,梁伟.股指期货预测模型构建及其应用效果分析[J].统计与决策.2009,287(13)：19-22.
[44]何平,孟庆跃.基于误差修正模型的卫生总费用与GDP关系研究[J].中国卫生经济.2005,24(9)：20-22.
[45]何平平.经济增长、人口老龄化与医疗费用增长—中国数据的计量分析[J].财经理论与实践.2006,27(140)：90-94.
[46]Getzen T E. Population aging and the growth of health expenditures [J]. The Journal of Gerontology.1992,47(3):S98-S104.
[47]李军山,江可申,李雪强.我国医疗费用增长因素实证分析[J].经济分析.2008,377(10)：56-58.
[48]Chris Chatfield. Time series forecasting [M].New York: CHAPMAN & HALL/ CRC：1-10.
[49]杨功焕.中国人群死亡及其危险因素、流行水平、趋势和分布[M].北京：中国协和医科大学出版社,2005.
[50]赵文华,翟屹,胡建平等.中国超重和肥胖造成相关慢性疾病的经济负担研究[J].中华流行病学杂志.2006,27(7)：555-559.
[51]雷海潮,刘兴柱,卞鹰.慢性病增加对我国卫生总费用及宏观经济的影响研究[J].中国卫生经济.1996,164(15)：27-28.
[52]顾昕.走向全民医保—中国新医改的战略与战术[M].北京：中国劳动社会保障出版社,2008：111-117.
[53]卫生部卫生经济研究所.2010年中国卫生总费用研究报告[R].北京：2010.
[54]Durbin, J. and S. J. Koopman. Time series analysis by state space methods. Number 24 in Oxford Statistical Science Series. Oxford: Oxford University Press:53.
[55]Jacques J.F. Commandeur, Siem Jan Koopman. An Introduction to State Space Time Series Analysis [M]. New York: Oxford University Press,122-169.
[56]高铁梅.计量经济分析方法与建模—EViews应用及实例[M].北京：清华大学出版社：353-379.
[57]Ruey S. Tsay. Analysis of financial time series [M]. New Jersey: John Wiley & Sons, Inc.2005:454-459.
[1]张振忠,王禄生,杨洪伟.中国卫生费用核算研究报告[M].北京：人民卫生出版社,2009.8.
[2]卫生部卫生经济研究所.2008年中国卫生总费用研究报告[R].北京：2008：100-102.
[3]王颖.卫生总费用研究进展与思考[J].中国卫生事业管理.2003,179(5)：267-268.
[4]杜乐勋,赵郁馨,刘国祥.建国60年政府卫生投入和卫生总费用核算的回顾与展望[J].中国卫生政策研究.2009,10(2)：15-20.
[5]International Labor Organization. The cost medical of care [R]. Geneva,1959.
[6]Joseph P. Newhouse. Medical-Care Expenditure:A cross-national Survey [J].The Journal of Human Resources,1977,12(1):115-125.
[7]Leu R E. The Public-Private mix and international health care costs [J]. Journal of Health Economics.1986,79(2):251-277.
[8]Gerdtham U G, Jonsson B. An econometric analysis of health care expenditures, A cross section study of OECD countries [J]. Journal of Health Economics,1992,11(1):63-68.
[9]Hitiris T, Posnet J. The determinants and the effects of health expenditure in developed countries [J]. Journal of Health Economics,1992,11(2):173-181.
[10]Getzen T E. Population aging and the growth of health expenditures [J]. The Journal of Gerontology.1992,47(3):S98-S 104.
[11]Francia L, Gitto L, Mennini F, Polistena B. An econometric analysis of OECD Countries health expenditure. iHEA 2007 6th World Congress:Explorations in Health Economics Paper[R]. Social Science Research Network. http://papers.ssm.com
[12]庞浩,李南成.计量经济学[M].四川：西南财经大学出版社,2002,239-242.
[13]赫尔穆特.鲁克波尔(德),马库斯.克莱茨希(德)编,易行健,邓可斌等译.应用时间序列计量经济学[M].北京：机械工业出版社,2008：68-70.
[14]Granger, C.W.J and Newbold, P. Spurious regressions in econometrics, Journal of Econometrics [J].1974,(2):111-120.
[15]Granger, C.W.J and Engle, R.F. Application of spectral analysis in econometrics in D.R. Brillinger and P. R. Kishnaiah (eds) Handbook of Statistics, Amsterdam [J],1984,(5), Elsevier.
[16]Hansen P, King A. The determinants of health care expenditure:A co integration approach[J]. Journal of Health Economics [J].1996,(15):127-137.
[17]Blomqvist, A.G., Carter, R.A. Is health care really a luxury? [J]. Journal of Health Economics.16(2):207-229.
[18]McCoskey S.K, Selden T.M. Health care expenditures and GDP:Panel data unit root test results [J]. Journal of Health Economics.1998,17(3):369-376.
[19]Gerdtham UG, Lothgren M. On stationarity and co integration of international health expenditure and GDP [J]. Journal of Health Economics. 2000,19(4):461-475.
[20]Jewell T, Lee J, Tieslau M, Strazicich M.C. Stationarity, health expenditures and GDP:Evidence from panel unit root tests with heterogeneous structural breaks [J].Journal of Health Economics.2003,17(3):313-323.
[21]Im K.S., Lee J. Tieslau M, Panel LM unit root tests with level shifts [J]. Oxford Bulletin of Economics & Statistics.2005,67(3):393-419.
[22]Okunade A.,Karakus M. C. Unit root and cointegration tests:Timeseries versus panel estimates for international health expenditure models [J]. 2001,33(9):1131-1137.
[23]Dreger C, Reimers H.E. Health care expenditures in OECD Countries:A panel unit root and co integration analysis. Social Science Research Network, IZA Discussion Paper No.1469,2005.
[24]Pedroni, Perer. Critical values for co integration tests in heterogeneous panels with multiple regressors[J]. Oxford Bulletin of Economics and Statistics.1999,61, Special Issue,653-670.
[25]Kao, Chiwa, McCoskey, Suzanne. A residual-based test of the null of cointegration in panel data[J].Econometric Reviews.1998,17:57-84.
[26]Banerjee A, Marcellino M,Osbat C.Some Cautious on the use of panel methods for integrated series of macroeconomic data. IGIER Working paper No.170,2001.
[27]Urbain, J.P. Spurious regression in nonstationary panels with cross-unit co integration.2004,manuscript.
[28]Levin A,Lin C and Chu J. Unit root tests in panel data: Asymptotic and finite-sample properties [J]. Journal of Economics,2002,108:1-24.
[29]Im K.S, Pesaran M,H and Shin Y. Testing for unit roots in heterogeneous panels [J], Journal of Econometrics,2003,115:53-74.
[30]Karlsson, S. and Lothgren, M. On the power and interpretation of panel unit root tests [J].Economics Letters,2000,66,249-255.
[31]Herwartz H, Theilen B. The determinants of health care expenditure:Testing pooling restrictions in small samples. Berlin,2000,1-30.
[32]Devlin N, Hansen P. Health care spending and economic output granger causality[J]. Applied Economics.2001,(8):561-564.
[33]Murthy N.R.V, Ukpolo V. Aggregate health care expenditures in the United Stated: Evidence from co integration tests [J]. Applied Economics,1994,26(8):797-802.
[34]Getzen T E. Forecasting health expenditures:Short, medium and long Term [J]. Journal of Healthcare Finance.2000,26(3):56-72.
[35]Das A, Martin F. An econometric analysis of the US health care expenditure [J]. Global Journal of the Health Science [J].2010,2(1):150-159.
[36]Peden EA, Freeland M. Insurance effects on U.S. medical spending 1960-1993 [J]. Health Economics.1998,(7):671-687.
[37]Singer, B.H. and K.G. Manton. "The effects of health changes on projections of health service needs for the elderly population of the United States" [J]. Proceedings of the National Academy of Science.1998,95:15618-15622.
[38]Fuches V R. Forecasting health expenditures:Short, medium and long term [J]. Journal of Healthcare Finance,1998,26(3):56-72.
[39]Technical review panel on the medicare trustees reports[R]. U.S:Department of Health & Human Services,2000:49.
[40]Cutler D, and L. Sheiner. Demographic and medical care spending: Standard and non-standard effects[M]. Cambridge: Cambridge University Press.2000,253-291.
[41]Lee R, and Miller T. An approach to forecasting health expenditures, with application to the U.S. Medicare system [J].2002,37(5):1365-1386.
[42]Richard B, Saltman,et.A methodological note on combining health and social care expenditures into a single statistic for policy-making purposes [J].European Journal of Public Health.2001,11(1):93-96.
[43]赵郁馨,高广颖,杜乐勋.中国卫生总费用发展变化趋势及其影响因素[J].卫生经济研究,2000,(1)：7-9.
[44]何平平.中国卫生总费用增长因素研究[J].统计与信息论坛.2006,21(1)：37-41.
[45]于德志.我国卫生费用增长分析[J].中国卫生经济.2005,24(3)：5-7.
[46]何平,孟庆跃.基于误差修正模型的卫生总费用与GDP关系研究[J].中国卫生经济.2005,24(9)：20-22.
[47]李军山,江可申,李雪强.我国医疗费用增长因素实证分析[J].经济分 析.2008,377(10)：56-58.
[48]何平平.经济增长、人口老龄化与医疗费用增长—中国数据的计量分析[J].财经理论与实践.2006,27(140)：90-94.
[49]候文,荏苒.中国卫生总费用与GDP协整和引导关系的实证分析[J].辽宁师范大学学报(自然科学版).2008,31(1)：12-14.
[50]刘柏,赵振全,汪玲丽等.我国THE和GDP之间影响关系的2个区间实证检验[J].中国卫生经济.2006,25(1)：36-39.
[51]雷海潮.中国卫生总费用占国内生产总值比例的预测研究[J].中国卫生经济,1998,1(8)：5-8.
[52]李亚青.应用SimFin模型预测卫生总费用占GDP比值的变化趋势[J].中国卫生资源.2002,5(1)：29-30.
[53]刘明霞,任仕泉.自组织数据挖掘在卫生总费用预测中的应用[J].卫生经济研究,2003,12：10.
[2]卫生部卫生经济研究所.2008 年中国卫生总费用研究报告[R].北京：2008：100-102.
[3]Joseph P. Newhouse. Medical-Care Expenditure: A cross-national Survey [J].The Journal of Human Resources.1977,12(1):115-125.
[4]Leu R E. The Public-Private mix and international health care costs [J]. Journal of Health Economics.1986,79(2):251-277.
[5]Gerdtham U G, Jonsson B. An econometric analysis of health care expenditures, A cross section study of OECD countries [J]. Journal of Health Economics, 1922,11(1):63-68.
[6]Hitiris T, Posnet J. The determinants and the effects of health expenditure in developed countries [J]. Journal of Health Economics,1992,11(2):173-181.
[7]Francia L, Gitto L, Mennini F, Polistena B. An econometric analysis of OECD Countries health expenditure. iHEA 2007 6th World Congress:Explorations in Health Economics Paper[R]. Social Science Research Network. http://papers.ssm.com
[8]Granger, C.W.J and Newbold, P. Spurious regressions in econometrics, Journal of Econometrics [J],1974,(2):111-120.
[9]Granger, C.W.J and Engle, R.F. Application of spectral analysis in econometrics in D.R. Brillinger and P. R. Kishnaiah (eds) Handbook of Statistics, Amsterdam [J],1984,(5), Elsevier.
[10]Hansen P, King A. The determinants of health care expenditure:A co integration approach[J]. Journal of Health Economics [J].1996,(15):127-137.
[11]Blomqvist, A.G, Carter, R.A. Is health care really a luxury? [J]. Journal of Health Economics.16(2):207-229.
[12]McCoskey S.K, Selden T.M. Health care expenditures and GDP:Panel data unit root test results [J]. Journal of Health Economics.1998,17(3):369-376.
[13]Jewell T, Lee J, Tieslau M, Strazicich M.C. Stationarity, health expenditures and GDP:Evidence from panel unit root tests with heterogeneous structural breaks [J].Journal of Health Economics.2003,17(3):313-323.
[14]Im K.S., Lee J. Tieslau M, Panel LM unit root tests with level shifts [J]. Oxford Bulletin of Economics & Statistics.2005,67(3):393-419.
[15]Okunade A.,Karakus M. C. Unit root and cointegration tests: Timeseries versus panel estimates for international health expenditure models [J]. 2001,33(9):1131-1137.
[16]Dreger C, Reimers H.E. Health care expenditures in OECD Countries:A panel unit root and co integration analysis. Social Science Research Network, IZA Discussion Paper No.1469,2005.
[17]Pedroni, Perer. Critical values for co integration tests in heterogeneous panels with multiple repressors [J]. Oxford Bulletin of Economics and Statistics. 1999,61, Special Issue,653-670.
[18]Kao, Chiwa, McCoskey, Suzanne. A residual-based test of the null of co integration in panel data[J].Econometric Reviews.1998,17:57-84.
[19]Murthy N.R.V, Ukpolo V. Aggregate health care expenditures in the United Stated: Evidence from co integration tests [J]. Applied Economics,1994,26(8):797-802.
[20]Das A, Martin F. An econometric analysis of the US health care expenditure [J]. Global Journal of the Health Science [J].2010,2(1):150-159.
[21]Fuches V R. Forecasting health expenditures:Short, medium and long term [J]. Journal of Healthcare Finance,1998,26(3):56-72.
[22]Richard B, Saltman,et.A methodological note on combining health and social care expenditures into a single statistic for policy-making purposes [J].European Journal of Public Health.2001,11(1):93-96.
[23]何平平.中国卫生总费用增长因素研究[J].统计与信息论坛.2006,21(1)：37-41.
[24]何平,孟庆跃.基于误差修正模型的卫生总费用与GDP关系研究[J].中国卫生经济.2005,24(9)：20-22.
[25]候文,荏苒.中国卫生总费用与GDP协整和引导关系的实证分析[J].辽宁师范大学学报(自然科学版).2008,31(1)：12-14.
[26]刘柏,赵振全,汪玲丽等.我国THE和GDP之间影响关系的2个区间实证检验[J].中国卫生经济.2006,25(1)：36-39.
[27]雷海潮.中国卫生总费用占国内生产总值比例的预测研究[J].中国卫生经济,1998,1(8)：5-8.
[28]李亚青.应用SimFin模型预测卫生总费用占GDP比值的变化趋势[J].中国卫生资源.2002,5(1)：29-30.
[29]王燕.应用时间序列分析[M].北京：中国人民大学出版社.2005：209-230.
[30]易丹辉.数据分析与Eviews应用[M].北京：中国统计出版社,2002：150.
[31]庞皓,李南成.计量经济学[M].四川：西南财经大学出版社,2005：239-242.
[32]Granger, C. W. J. Investigating Causal Relations by Econometric Models and Cross-spectral Methods[J]. Econometrica,37(3):424-438.
[33]邹平.金融计量学[M].上海：上海财经大学出版社有限公司,2010：174-175.
[34]Bovas, A, Johannes, L. Statistical methods for forecasting [M]. New Jersey: John Wiley & Sons, Inc.2005:2-3.
[35]Montgomery, D.C., Jennings, C.L., Introduction to time series analysis and forecasting [M]. Hoboken, NJ,2008:34.
[36]Fox, J. Applied regression analysis and generalized linear model (2nd ed.)[M]. los Angeles,2008.
[37]Spanos, A. Statistical foundations of econometric modeling [M]. New York: Cambridge University Press.
[38]West, M., & Harrison, J. Bayesian forecasting and dynamic models (2nd ed.)[M].New York: Springer.
[39]高慧璇等编译.SAS系统SAS/ETS软件使用手册[M].北京：中国统计出版社,1998：481-482.
[40]Andrew. H, Siem. J.K, Neil. S. State space and unobserved component models [M]. New York:Cambridge University Press.2004:5-11.
[41]赫尔穆特.鲁克波尔(德),马库斯.克莱茨希(德)编,易行健,邓可斌等译.应用时间序列计量经济学[M].北京：机械工业出版社,2008：68-70.
[42]Rob J. Hyndman, Anne B. Koehler, J. Keith Ord. Forecasting with exponential smoothing-the state space approach [M]. Berlin: Springer:2008:25-27.
[43]薛勇,郭菊娥,梁伟.股指期货预测模型构建及其应用效果分析[J].统计与决策.2009,287(13)：19-22.
[44]何平,孟庆跃.基于误差修正模型的卫生总费用与GDP关系研究[J].中国卫生经济.2005,24(9)：20-22.
[45]何平平.经济增长、人口老龄化与医疗费用增长—中国数据的计量分析[J].财经理论与实践.2006,27(140)：90-94.
[46]Getzen T E. Population aging and the growth of health expenditures [J]. The Journal of Gerontology.1992,47(3):S98-S104.
[47]李军山,江可申,李雪强.我国医疗费用增长因素实证分析[J].经济分析.2008,377(10)：56-58.
[48]Chris Chatfield. Time series forecasting [M].New York: CHAPMAN & HALL/ CRC：1-10.
[49]杨功焕.中国人群死亡及其危险因素、流行水平、趋势和分布[M].北京：中国协和医科大学出版社,2005.
[50]赵文华,翟屹,胡建平等.中国超重和肥胖造成相关慢性疾病的经济负担研究[J].中华流行病学杂志.2006,27(7)：555-559.
[51]雷海潮,刘兴柱,卞鹰.慢性病增加对我国卫生总费用及宏观经济的影响研究[J].中国卫生经济.1996,164(15)：27-28.
[52]顾昕.走向全民医保—中国新医改的战略与战术[M].北京：中国劳动社会保障出版社,2008：111-117.
[53]卫生部卫生经济研究所.2010年中国卫生总费用研究报告[R].北京：2010.
[54]Durbin, J. and S. J. Koopman. Time series analysis by state space methods. Number 24 in Oxford Statistical Science Series. Oxford: Oxford University Press:53.
[55]Jacques J.F. Commandeur, Siem Jan Koopman. An Introduction to State Space Time Series Analysis [M]. New York: Oxford University Press,122-169.
[56]高铁梅.计量经济分析方法与建模—EViews应用及实例[M].北京：清华大学出版社：353-379.
[57]Ruey S. Tsay. Analysis of financial time series [M]. New Jersey: John Wiley & Sons, Inc.2005:454-459.
[1]张振忠,王禄生,杨洪伟.中国卫生费用核算研究报告[M].北京：人民卫生出版社,2009.8.
[2]卫生部卫生经济研究所.2008年中国卫生总费用研究报告[R].北京：2008：100-102.
[3]王颖.卫生总费用研究进展与思考[J].中国卫生事业管理.2003,179(5)：267-268.
[4]杜乐勋,赵郁馨,刘国祥.建国60年政府卫生投入和卫生总费用核算的回顾与展望[J].中国卫生政策研究.2009,10(2)：15-20.
[5]International Labor Organization. The cost medical of care [R]. Geneva,1959.
[6]Joseph P. Newhouse. Medical-Care Expenditure:A cross-national Survey [J].The Journal of Human Resources,1977,12(1):115-125.
[7]Leu R E. The Public-Private mix and international health care costs [J]. Journal of Health Economics.1986,79(2):251-277.
[8]Gerdtham U G, Jonsson B. An econometric analysis of health care expenditures, A cross section study of OECD countries [J]. Journal of Health Economics,1992,11(1):63-68.
[9]Hitiris T, Posnet J. The determinants and the effects of health expenditure in developed countries [J]. Journal of Health Economics,1992,11(2):173-181.
[10]Getzen T E. Population aging and the growth of health expenditures [J]. The Journal of Gerontology.1992,47(3):S98-S 104.
[11]Francia L, Gitto L, Mennini F, Polistena B. An econometric analysis of OECD Countries health expenditure. iHEA 2007 6th World Congress:Explorations in Health Economics Paper[R]. Social Science Research Network. http://papers.ssm.com
[12]庞浩,李南成.计量经济学[M].四川：西南财经大学出版社,2002,239-242.
[13]赫尔穆特.鲁克波尔(德),马库斯.克莱茨希(德)编,易行健,邓可斌等译.应用时间序列计量经济学[M].北京：机械工业出版社,2008：68-70.
[14]Granger, C.W.J and Newbold, P. Spurious regressions in econometrics, Journal of Econometrics [J].1974,(2):111-120.
[15]Granger, C.W.J and Engle, R.F. Application of spectral analysis in econometrics in D.R. Brillinger and P. R. Kishnaiah (eds) Handbook of Statistics, Amsterdam [J],1984,(5), Elsevier.
[16]Hansen P, King A. The determinants of health care expenditure:A co integration approach[J]. Journal of Health Economics [J].1996,(15):127-137.
[17]Blomqvist, A.G., Carter, R.A. Is health care really a luxury? [J]. Journal of Health Economics.16(2):207-229.
[18]McCoskey S.K, Selden T.M. Health care expenditures and GDP:Panel data unit root test results [J]. Journal of Health Economics.1998,17(3):369-376.
[19]Gerdtham UG, Lothgren M. On stationarity and co integration of international health expenditure and GDP [J]. Journal of Health Economics. 2000,19(4):461-475.
[20]Jewell T, Lee J, Tieslau M, Strazicich M.C. Stationarity, health expenditures and GDP:Evidence from panel unit root tests with heterogeneous structural breaks [J].Journal of Health Economics.2003,17(3):313-323.
[21]Im K.S., Lee J. Tieslau M, Panel LM unit root tests with level shifts [J]. Oxford Bulletin of Economics & Statistics.2005,67(3):393-419.
[22]Okunade A.,Karakus M. C. Unit root and cointegration tests:Timeseries versus panel estimates for international health expenditure models [J]. 2001,33(9):1131-1137.
[23]Dreger C, Reimers H.E. Health care expenditures in OECD Countries:A panel unit root and co integration analysis. Social Science Research Network, IZA Discussion Paper No.1469,2005.
[24]Pedroni, Perer. Critical values for co integration tests in heterogeneous panels with multiple regressors[J]. Oxford Bulletin of Economics and Statistics.1999,61, Special Issue,653-670.
[25]Kao, Chiwa, McCoskey, Suzanne. A residual-based test of the null of cointegration in panel data[J].Econometric Reviews.1998,17:57-84.
[26]Banerjee A, Marcellino M,Osbat C.Some Cautious on the use of panel methods for integrated series of macroeconomic data. IGIER Working paper No.170,2001.
[27]Urbain, J.P. Spurious regression in nonstationary panels with cross-unit co integration.2004,manuscript.
[28]Levin A,Lin C and Chu J. Unit root tests in panel data: Asymptotic and finite-sample properties [J]. Journal of Economics,2002,108:1-24.
[29]Im K.S, Pesaran M,H and Shin Y. Testing for unit roots in heterogeneous panels [J], Journal of Econometrics,2003,115:53-74.
[30]Karlsson, S. and Lothgren, M. On the power and interpretation of panel unit root tests [J].Economics Letters,2000,66,249-255.
[31]Herwartz H, Theilen B. The determinants of health care expenditure:Testing pooling restrictions in small samples. Berlin,2000,1-30.
[32]Devlin N, Hansen P. Health care spending and economic output granger causality[J]. Applied Economics.2001,(8):561-564.
[33]Murthy N.R.V, Ukpolo V. Aggregate health care expenditures in the United Stated: Evidence from co integration tests [J]. Applied Economics,1994,26(8):797-802.
[34]Getzen T E. Forecasting health expenditures:Short, medium and long Term [J]. Journal of Healthcare Finance.2000,26(3):56-72.
[35]Das A, Martin F. An econometric analysis of the US health care expenditure [J]. Global Journal of the Health Science [J].2010,2(1):150-159.
[36]Peden EA, Freeland M. Insurance effects on U.S. medical spending 1960-1993 [J]. Health Economics.1998,(7):671-687.
[37]Singer, B.H. and K.G. Manton. "The effects of health changes on projections of health service needs for the elderly population of the United States" [J]. Proceedings of the National Academy of Science.1998,95:15618-15622.
[38]Fuches V R. Forecasting health expenditures:Short, medium and long term [J]. Journal of Healthcare Finance,1998,26(3):56-72.
[39]Technical review panel on the medicare trustees reports[R]. U.S:Department of Health & Human Services,2000:49.
[40]Cutler D, and L. Sheiner. Demographic and medical care spending: Standard and non-standard effects[M]. Cambridge: Cambridge University Press.2000,253-291.
[41]Lee R, and Miller T. An approach to forecasting health expenditures, with application to the U.S. Medicare system [J].2002,37(5):1365-1386.
[42]Richard B, Saltman,et.A methodological note on combining health and social care expenditures into a single statistic for policy-making purposes [J].European Journal of Public Health.2001,11(1):93-96.
[43]赵郁馨,高广颖,杜乐勋.中国卫生总费用发展变化趋势及其影响因素[J].卫生经济研究,2000,(1)：7-9.
[44]何平平.中国卫生总费用增长因素研究[J].统计与信息论坛.2006,21(1)：37-41.
[45]于德志.我国卫生费用增长分析[J].中国卫生经济.2005,24(3)：5-7.
[46]何平,孟庆跃.基于误差修正模型的卫生总费用与GDP关系研究[J].中国卫生经济.2005,24(9)：20-22.
[47]李军山,江可申,李雪强.我国医疗费用增长因素实证分析[J].经济分 析.2008,377(10)：56-58.
[48]何平平.经济增长、人口老龄化与医疗费用增长—中国数据的计量分析[J].财经理论与实践.2006,27(140)：90-94.
[49]候文,荏苒.中国卫生总费用与GDP协整和引导关系的实证分析[J].辽宁师范大学学报(自然科学版).2008,31(1)：12-14.
[50]刘柏,赵振全,汪玲丽等.我国THE和GDP之间影响关系的2个区间实证检验[J].中国卫生经济.2006,25(1)：36-39.
[51]雷海潮.中国卫生总费用占国内生产总值比例的预测研究[J].中国卫生经济,1998,1(8)：5-8.
[52]李亚青.应用SimFin模型预测卫生总费用占GDP比值的变化趋势[J].中国卫生资源.2002,5(1)：29-30.
[53]刘明霞,任仕泉.自组织数据挖掘在卫生总费用预测中的应用[J].卫生经济研究,2003,12：10.