医疗条件对居民死亡率的影响

医疗条件对居民死亡率影响的实证分析张旭、郝立斌、李洋张旭、郝立斌、李洋一、问题的提出改革开放以来，尤其是我国进入20世纪90年代以后，我国的经济以比以往更快的速度迅猛发展，GDP增长率在全球一直遥遥领先，GDP总额也在进入21世纪后一度超越英国，德国，日本成为全球GDP总额第二，发展中国家中难以撼动的第一的地位。与我国经济快速发展相适应，我国政府不断增加对医疗机构的投入，在一些三级甲等医院的医疗实施甚至可以与美国，挪威的现代化医疗设施相媲美，这些医疗设施的改善是否落到实处，是否真的造福了广大的居民是我们亟需弄明白的问题。二、模型设定为解决这个现实的问题，我们引入了X］（医疗机构数量）,x2（医疗从业人员数量），X3（医院的床位数）三个变量作为描述我国医疗条件变化的三个要素•丫（居民死亡率）作为医疗条件改善对居民的现实影响的被解释变量。假设各解释变量Xx,X2,X3对被解释变量丫均为一次的。我们构造模型如下：Y=Po+PiXi+P2X2+P3X3+Ui三、数据的收集我们小组通过访问国家统计局官网中华人民共和国国家统计局NationalBureauofStatisticsofthePeople'sRepublicofChinaggpa国家统计局闘gggpa国家统计局闘g翩发布强j统计动态局长致辞乍縫劉数据查询通知公告机构职能数据解读圏片劭闻统计百科统计词典常见问题解督网上办事统计咨询局长信箱公开指南公开目录公开年报浏览2013年国家统计年鉴中国统计年鉴未婭许可，本书的任何部分不*以任何方式在世界任何地区以任何文字翻印、拷贝、仿制或转栽。久M32073中国般辎、下载.与我们研究有关的数据，我们连续选取了1995年到2012年医疗机构的数量、医疗从业人员数量、医疗机构床位数，以及死亡率的统计数据，数据整理如下：年忻医疗机构数里（个）医疗就业人员埶里聞床位数（万张）居民死亡率r199599440967043%314.066.57谗恳10781316736097309.966.567997：1048G6768339&2313.456.51陆塑10428856863315314.306.601017673689438531S.90646^00010342296910383317.706.45*200110293146874527320.126.43"200210050046528674313.616.41*20038062436216971316.406.40^20048491406332739326.846.42loos&822066447246336.756.51^20069180976681184351.186.81*20079122636964貂9370.116.93^20088914807251803403.877.0620099165717781448441.667.082010936927820760247S.&87.1120119543898616040515.997.1420129602979116706572.487.16四、模型的估计与调整将所取得数据通过E-Views进行LS分析结果如下：DependentVariable:Y

Method:LeastSquaresDate:12/04/14Time:14:32Sample:19952012Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.C5.7208960.55008910.399950.0000X13.82E-071.31E-060.2908150.7755X2-1.76E-074.04E-07-0.4369880.6688X30.0050520.0041371.2213610.2421R-squared0.806760Meandependentvar6.694444AdjustedR-squared0.765351S.D.dependentvar0.296732S.E.ofregression0.143738Akaikeinfocriterion-0.848513Sumsquaredresid0.289250Schwarzcriterion-0.650652Loglikelihood11.63661Hannan-Quinncriter.-0.821230F-statistic19.48290Durbin-Watsonstat0.481062Prob(F-statistic)0.000029模型Y=5.720896+3.82E-07X1-1.76E-07X2+0.005052X3+UiSE=(1.31E-06)(4.04E-07)(0.004137)T=(0.290815)(-0.436988)(1.221361)R-squared=0.806760；F=19.48290；DW=0.481062残差图如下：（一）经济意义检验其他条件不变的情况下医疗机构数量每增加一家居民死亡率平均上升3.82E-07；其他条件不变的情况下医疗从业人员数量每增加一人居民死亡率平均下降1.76E-07；床位数每增加一万张居民死亡率平均上升0.005052。（二）统计意义检验1、可决系数检验R-squared=0.806760接近于1，可决系数检验方程较为显著。2、F-检验F=19.48290,查表得在置信度为0.05的情况下F值大于19.48290,该模型的F检验不显著。3、T-检验T（XJ=0.290815＜置信度为0.05情况下的T值，该系数不显著；T（X2）=0436988＜置信度为0.05情况下的T值，该系数不显著；T（Xg）=1.221361＜置信度为0.05情况下的T值，该系数不显著。（三）计量经济学意义检验1、多重共线性检验2、将所取得数据通过E-Views进行LS分析结果如下：DependentVariable:YMethod:LeastSquaresDate:12/04/14Time:14:32Sample:19952012Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.C5.7208960.55008910.399950.0000X13.82E-071.31E-060.2908150.7755X2-1.76E-074.04E-07-0.4369880.6688X30.0050520.0041371.2213610.2421R-squared0.806760Meandependentvar6.694444AdjustedR-squared0.765351S.D.dependentvar0.296732S.E.ofregression0.143738Akaikeinfocriterion-0.848513Sumsquaredresid0.289250Schwarzcriterion-0.650652Loglikelihood11.63661Hannan-Quinncriter.-0.821230F-statistic19.48290Durbin-Watsonstat0.481062Prob(F-statistic)0.000029由此可见R-squared=0.806760，AdjustedR-squared=0.765351，F-statistic=19.48290显著，但是x1、x2、x3不能通过T检验可能存在严重的多重共线性。X1,X2crosscorrelation图示如下bate:12/25/14Time:17:27Sample:19952012Includedobservations:18CorrelationsareasymptoticallyconsistentapproximationsX1,X2(+i}laglead00.03360.0336X1,X2(+i}laglead00.03360.033610.1006-0.104320.1199-0.200430.1258-0.43654-0.1410-0.552750.1505-0.606060.1554-0.691770.1700-0.7072801851-0.5491g0.1304-0.2491100.09260.2345110.06860.3408120.06210L3S68130.05070.4100U0.02810.4392150.0129OL3S19160.00520.2S0SX3,X2crosscorrelationDate:12/25^14Time:1735X3,X2crosscorrelationDate:12/25^14Time:1735Sample:19952012Includedobservations:18CorrelationsareasymptoticallyconsistentapproximationsX2,X3(-i)X2,X3(+i)ilaglead00.95410.954100.95410.954110.73630.637520.54950.436930.37520.199440.2216-0.011550.1041-0.153460.0274-0.25577-0.0321-0.30343-0.0917-0.29349-fl1541-0.256410-fl1524-0.163711-0.2216-0.110712-0.2527-0.120813-0.2673-0.1378U-0.2658-0.146615-0.2347-0.141516-0.1812-0.123433、异方差性检验HeteroskedasticityTest:WhiteF-statistic7.083565Prob.F(9,8)0.0056Obs*R-squared15.99309Prob.Chi-Square(9)0.0670ScaledexplainedSS5.743888Prob.Chi-Square(9)0.7653TestEquation:DependentVariable:RESIDEMethod:LeastSquaresDate:12/21/14Time:17:14Sample:19952012Includedobservations:18VariableCoefficientStd.Errort-StatisticProb.C-3.9901531.108572-3.5993640.0070X1A2-7.55E-133.13E-12-0.2413820.8153X1*X29.59E-131.97E-120.4862130.6399X1*X3-3.36E-092.12E-08-0.1583840.8781X1-3.96E-062.66E-06-1.4919550.1741X2A2-3.74E-133.38E-13-1.1067190.3006X2*X33.76E-096.99E-090.5380650.6052X22.84E-069.01E-073.1522730.0136X3A2-2.68E-073.66E-05-0.0073170.9943X3-0.0216030.009466-2.2821300.0519R-squared0.888505Meandependentvar0.016069AdjustedR-squared0.763073S.D.dependentvar0.018018S.E.ofregression0.008770Akaikeinfocriterion-6.334700Sumsquaredresid0.000615Schwarzcriterion-5.840049Loglikelihood67.01230Hannan-Quinncriter.-6.266494F-statistic7.083565Durbin-Watsonstat1.967133Prob(F-statistic)0.005612Obs*R-squared=15.99309<x2o.oj(4)=11-1433证明模型不存在异方差性。4、自相关检验DW-Stat=0.481062<dL残差项u〔、u2、…、un间存在正相关。

模型自相关修正（科克伦-奥科特迭代法）：DependentVariable:EMethod:LeastSquaresDate:12/25/14Time:16:54Sample(adjusted):19962012Includedobservations:17afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.E(-1)0.8011920.1829444.3794270.0005R-squared0.544781Meandependentvar-0.003873AdjustedR-squared0.544781S.D.dependentvar0.133384S.E.ofregression0.089994Akaikeinfocriterion-1.921123Sumsquaredresid0.129583Schwarzcriterion-1.872111Loglikelihood17.32955Hannan-Quinncriter.-1.916251Durbin-Watsonstat0.751970得到e=0.801192ett-1令Yt-0・801192Yt-仁0°(1-0.801192)+(^「0.801192X伯)+0(X0.801192X)+0(x-0.801192X)+v221-2t-1/3313t-1/tDependentVariable:Y-0.801192*Y(-1)Method:LeastSquaresDate:12/25/14Time:17:07Sample(adjusted):19962012Includedobservations:17afteradj