计量经济学论文(eviews分析)《促进内蒙古经济发展的因素分析》

促进内蒙古经济发展的因素分析摘要：“西部大开发”发展战略的实施西部落后地区迎来了发展的机遇，内蒙古作为西部的一个省，也将抓住机遇积极发展经济。近年来内蒙经济在全区的结构调整和西部大开发的过程中有了长足的发展,但是在发展中也存在一些问题和困难,需要我们认真重视、研究并加以改进,本文就影响内蒙古经济发展的因素进行了分析,并提出了一些的建议。发挥怎样才能使内蒙古更好更快地发展起来呢？在经济建设中又应当注重哪些方面呢？下面我对其影响经济的几个因素进行了分析。数据如下：(表1)（资料来源：内蒙古统计局、中国统计年鉴）其中：Y代表地区生产总值，X1代表社会消费品零售总额X2代表基本建设投资，X3代表固定资产投资总额，X4代表出口总额,X5代表进口总额，X6代表实际利用外资额,X7代表教育事业投资总额。年份生产总值（亿元）Y社会消费品零售总额(亿元)X1基本建设投资(亿元)X2固定资产投资(亿元)X3出口总额(亿元)Z1进口总额(亿元)Z2实际利用外资额(万美元)X5教育事业投资(亿元)X61985163.8375.63735.684852.424.38801.51735304.84201986181.5884.88097.293947.576.36562.54306645.57651987212.2796.30065.411453.328.43102.882011206.03451988270.81118.89675.016572.0510.93903.19139617.15701989292.69125.68754.585270.6812.51583.603330507.72451990319.31130.57604.941570.7716.94838.341525308.56471991359.66145.52076.2478100.6622.45979.709555328.58841992421.68168.68516.3988149.2431.916818.7901791010.02031993532.70202.14786.1791217.4056.184347.98071921312.24751994681.92247.29135.7617250.9951.337340.13122908615.04001995832.88295.01615.6159273.0650.678543.08866180116.55801996984.78335.36017.5551275.5456.913246.97823835519.778619971099.77367.91579.1114317.5060.945847.67304420920.245119981192.29400.887214.3054350.1668.163546.55384425324.218919991268.20437.381318.1797383.3775.002858.09584013327.515920001401.01483.981434.3664430.4284.711484.06975481929.752120011545.79537.307151.3916496.4394.3996116.50394734239.738920021734.31598.964681.2413715.09113.4776135.25035821148.284720032150.41726.763078.13121209.44119.2581138.43946652954.3525一、建立模型并回归Y=C+a1*X1+a2*X2+a3*X3+a4*X4+a5*X5+a6*X6+a7*X7+u运用OLS估计方法对式1中的参数进行估计，得回归分析结果：（表2）DependentVariable:YMethod:LeastSquaresDate:12/14/10Time:21:52Sample:19852003Includedobservations:19VariableCoefficientStd.Errort-StatisticProb.C-96.6873311.43839-8.4528780.0000X13.2436820.30628810.590300.0000X2-0.6908181.160908-0.5950670.5638X30.0236960.0472950.5010300.6262X4-0.1116520.947891-0.1177900.9084X5-0.6178430.841959-0.7338160.4784X60.0003350.0005510.6084760.5552X7-0.1115265.205167-0.0214260.9833R-squared0.999523Meandependentvar823.4679AdjustedR-squared0.999220S.D.dependentvar597.4553S.E.ofregression16.68431Akaikeinfocriterion8.762376Sumsquaredresid3062.028Schwarzcriterion9.160035Loglikelihood-75.24257F-statistic3295.807Durbin-Watsonstat1.297381Prob(F-statistic)0.000000从表2中可以看出F检验显著，但有几项t检验不过关，说明变量之间存在多重线性。为此我们进行如下操作：表2可以看出x7的t检验的p值最大，因此将x7因素剔除再进行回归分析。运用OLS估计方法剔除x7的方程进行估计，得回归分析结果：（表3）DependentVariable:YMethod:LeastSquaresDate:12/14/10Time:21:57Sample:19852003Includedobservations:19VariableCoefficientStd.Errort-StatisticProb.C-96.6978310.94161-8.8376230.0000X13.2378720.13640723.736910.0000X2-0.7088930.763578-0.9283830.3715X30.0235050.0444750.5285150.6068X4-0.1110900.907207-0.1224520.9046X5-0.6216090.788372-0.7884720.4457X60.0003390.0005000.6776230.5109R-squared0.999523Meandependentvar823.4679AdjustedR-squared0.999285S.D.dependentvar597.4553S.E.ofregression15.97434Akaikeinfocriterion8.657155Sumsquaredresid3062.156Schwarzcriterion9.005106Loglikelihood-75.24297F-statistic4194.488Durbin-Watsonstat1.298786Prob(F-statistic)0.000000从表3中可以看出F检验显著，但有几项t检验不过关，说明变量之间存在多重线性。为此我们进行如下操作：表2可以看出x4的t检验的p值最大，因此将x4因素剔除再进行回归分析。运用OLS估计方法剔除x4的方程进行估计，得回归分析结果：（表4）DependentVariable:YMethod:LeastSquaresDate:12/14/10Time:21:58Sample:19852003Includedobservations:19VariableCoefficientStd.Errort-StatisticProb.C-96.9699810.29963-9.4148990.0000X13.2275750.10324931.259970.0000X2-0.6465820.547317-1.1813650.2586X30.0232500.0427090.5443830.5954X5-0.7007070.434512-1.6126300.1308X60.0003470.0004760.7293490.4787R-squared0.999523Meandependentvar823.4679AdjustedR-squared0.999339S.D.dependentvar597.4553S.E.ofregression15.35724Akaikeinfocriterion8.553140Sumsquaredresid3065.982Schwarzcriterion8.851384Loglikelihood-75.25483F-statistic5446.027Durbin-Watsonstat1.273760Prob(F-statistic)0.000000从表4中可以看出F检验显著，但有几项t检验不过关，说明变量之间存在多重线性。为此我们进行如下操作：表2可以看出x3的t检验的p值最大，因此将x3因素剔除再进行回归分析。运用OLS估计方法剔除x3的方程进行估计，得回归分析结果：（表5）DependentVariable:YMethod:LeastSquaresDate:12/14/10Time:21:59Sample:19852003Includedobservations:19VariableCoefficientStd.Errort-StatisticProb.C-98.761789.511131-10.383810.0000X13.2539880.08881936.636310.0000X2-0.5217370.484304-1.0772920.2996X5-0.7421630.416898-1.7802060.0967X60.0003590.0004640.7747250.4514R-squared0.999512Meandependentvar823.4679AdjustedR-squared0.999372S.D.dependentvar597.4553S.E.ofregression14.96633Akaikeinfocriterion8.470418Sumsquaredresid3135.876Schwarzcriterion8.718954Loglikelihood-75.46897F-statistic7167.712Durbin-Watsonstat1.232880Prob(F-statistic)0.000000从表5中可以看出F检验显著，但有几项t检验不过关，说明变量之间存在多重线性。为此我们进行如下操作：表2可以看出x6的t检验的p值最大，因此将x6因素剔除再进行回归分析。运用OLS估计方法剔除x6的方程进行估计，得回归分析结果：（表6）DependentVariable:YMethod:LeastSquaresDate:12/14/10Time:22:00Sample:19852003Includedobservations:19VariableCoefficientStd.Errort-StatisticProb.C-101.77528.562932-11.885560.0000X13.2965350.06886847.867530.0000X2-0.7567730.372442-2.0319230.0603X5-0.6335780.387363-1.6356190.1227R-squared0.999491Meandependentvar823.4679AdjustedR-squared0.999389S.D.dependentvar597.4553S.E.ofregression14.76553Akaikeinfocriter