计量经济学分析报告

摘要：本文利用我国1985年以来的统计数字建立了可以通过各种检验的居出自己的一些建议。关键词：居民消费价格指数城镇居民农村居民一、引言CPI容，那就是居民日常消费的全部商品和效劳工程。日常生活中，我国城乡居民消生，我们所理解的并不彻底，我们所能涉及的范围也很小，所以借由国家统计数据做以下分析，促使我们更好的把握专业学问，了解国情，提高我们实际操作水平和理论联系实际、觉察问题、分析问题、解决问题的力气。二、影响因素的分析居民消费价格指数是反映确定时期内居民消费价格变动趋势和变动程度的3252731636影响：1、城镇居民消费价格指数〔y1〕2、农村居民消费价格指数〔y2〕3、城镇居民人均消费支出〔x1〕4、农村居民人均消费支出〔x2〕5、其他因素〔u〕三、模型：1、本文模型数据样本从1985—2023年：Y居民消费价格指数Y1城镇居民消费价格指数Y2农村居民消费价格指数X1城镇居民人均消费支出X2农村居民人均消费支出年份居民消费价格指数城镇居民消费价格指数农村居民消费价格指数城镇居民人均消费支出农村居民人均生活消费支出1985109.3111.9107.67653491986106.5107106.18723781987107.3108.8106.29984211988118.8120.7117.513115091989118116.3119.314665491990103.1101.3104.515965601991103.4105.1102.318406021992106.4108.6104.722626881993114.7116.1113.729248051994124.1125123.4385210381995117.1116.8117.5493113131996108.3108.8107.9553216261997102.8103.1102.558231722199899.299.49961091730199998.698.798.5640517662023100.4100.899.9685018602023100.7100.7100.871131969202399.29999.6738720622023101.2100.9101.6790121032023103.9103.3104.8867923012023101.8101.6102.2941025602023101.5101.5101.51035928482、基于以上数据，建立一下模型：Y=β+β1

y+β21

y+β32

x+β41

x+u52y的格兰杰缘由Yy1PairwiseGrangerCausalityTestsDate:12/22/10Time:12:13Sample:19852023Lags:2F-StatistNullHypothesis:ObsicProb.Y1doesnotGrangerCauseY204.561200.0283YdoesnotGrangerCauseY13.373640.0617P=0.0283<0.05显著，y1是y的格兰杰缘由Yy2PairwiseGrangerCausalityTestsDate:12/22/10 Time:12:13Sample:19852023Lags:2F-StatistNullHypothesis: Obs ic Prob.Y2doesnotGrangerCauseY203.864840.0443YdoesnotGrangerCauseY25.070540.0208P=0.0443<0.05显著，y2是y的格兰杰缘由Yx1PairwiseGrangerCausalityTestsDate:12/22/10Time:12:13Sample:19852023Lags:2F-StatistNullHypothesis:ObsicProb.X1doesnotGrangerCauseY2011.17810.0011YdoesnotGrangerCauseX12.808210.0921P=0.0011<0.05显著，x1是y的格兰杰缘由Yx2PairwiseGrangerCausalityTestsDate:12/22/10 Time:12:13Sample:19852023Lags:2NullHypothesis:ObsF-StatisticProb.X2doesnotGrangerCauseY207.787390.0048YdoesnotGrangerCauseX21.286020.3052P=0.0048<0.05显著，x2是y的格兰杰缘由经过格兰杰检验，4个解释变量均为y的格兰杰缘由，可以作为解释变量②一般最小二乘法DependentVariable:YMethodLeastSquaresDate:12/22/10 Time:12:25Sample:19852023Includedobservations:22CoefficieVariable ntStd.Errort-Statistic Prob.-0.03968C 20.405396-0.097885 0.9232Y10.4325840.01080240.048350.0000Y20.5670860.01074052.800870.0000X1-9.56E-050.000104-0.9155660.3727X2 0.0003570.0004140.863621 0.3998Meandependent 106.650Adjusted

0.999884varS.D.dependent0.999856varAkaikeinfo

07.370388-1.8176S.E.ofregression0.088378criterionSumsquaredresid 0.132781 SchwarzcriterionHannan-QuinnLoglikelihood 24.99445criter.Durbin-WatsonF-statistic 36509.31statProb(F-statistic) 0.000000

77-1.569713-1.7592641.420561由以上分析，初步建立模型为：Y=-0.039682+0.432584*y1+0.567086*y2-9.56*x1+0.000357*x2R2的拟合程度为0.999884 F=36509.31125125120115110105.210095.1.0-.1-.286889092 94969800020406ResidualActualFittedⅠ、white检验HeteroskedasticityTest:WhiteF-statistic 0.527426 Prob.F(14,7)Prob.Obs*R-squared 11.29363Chi-Square(14)

0.85400.6628ScaledexplainedSS

Prob.6.684989Chi-Square(14) 0.9462TestEquation:DependentVariable:RESID^2Method:LeastSquaresDate:12/22/10 Time:12:30Sample:19852023Includedobservations:22VariableCoefficientStd.Errort-StatisticProb.-0.79351C61.975576-0.4016630.6999Y10.0106650.0243870.4373000.6751Y1^20.0009500.0008311.1440120.2902-0.00196Y1*Y200.001686-1.1620230.2833Y1*X19.39E-061.67E-050.5621840.5915Y1*X2-3.22E-056.65E-05-0.4842290.6430-0.00017Y220.035384-0.0048570.9963Y2^20.0009720.0008521.1412930.2913Y2*X1-4.95E-061.69E-05-0.2933410.7778Y2*X21.47E-056.91E-050.2129090.8375-0.00062X190.000692-0.9090630.3935-4.47E-0X1^291.25E-07-0.0357820.9725X1*X21.39E-079.85E-070.1410530.8918X20.0025330.0028390.8920480.4020-4.71E-0X2^271.95E-06-0.2409810.8165Adjusted

Meandependent0.513347var-0.45995 S.D.dependent9var

0.0060350.008698AkaikeinfoS.E.ofregression0.010510criterionSumsquaredresid 0.000773 SchwarzcriterionHannan-QuinnLoglikelihood 81.59905criter.Durbin-WatsonF-statistic 0.527426stat0.85397Prob(F-statistic) 3

-6.054459-5.310566-5.8792203.341695由于Obs*R-squared=11.29363>卡方0.05〔5〕=11.07，所以存在异方差，用加权最小二乘法消退异方差DependentVariable:YMethodLeastSquaresDate:12/22/10 Time:12:37Sample:19852023Includedobservations:22Weightingseries:WWeighttype:Inversestandarddeviation(EViewsdefaultscaling)VariableCoefficientStd.Errort-StatisticProb.-0.13124C40.136038-0.9647570.3482Y10.4299470.002023214.11690.0000Y20.5703750.002160264.06220.0000-0.00013X173.91E-05-3.4997210.0027X20.0005170.0001573.2961810.0043WeightedStatisticsAdjusted

Meandependent0.999989varS.D.dependent0.999986var

106.6536129.1117AkaikeinfoS.E.ofregression0.027333criterionSumsquaredresid 0.012700 SchwarzcriterionHannan-QuinnLoglikelihood 50.81234criter.Durbin-WatsonF-statistic 384834.4statWeightedmeanProb(F-statistic) 0.000000dep.Statistics

-4.164758-3.916794-4.1063450.860182106.0005Adjusted

Meandependent0.999882varS.D.dependent

106.6500R-squared 0.999854var 7.370388S.E.ofregression0.089116 Sumsquaredresid 0.135010Durbin-Watsonstat 1.385850分析各变量是否存在相关性，并予以消退DU=1.80 0<DW<DL，所以，存在一介正自相关差分—消退DependentVariable:DYMethod:LeastSquaresDate:12/22/10 Time:12:50Sample(adjusted):19862023Includedobservations:21afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C0.0043600.0453690.0960980.9246DY10.4259380.01057740.270640.0000DY20.572954-9.20E-00.01106251.795730.0000DX150.000170-0.5405270.5963DX20.0003670.0005000.7335280.4738R-squared

0.999746 Meandependentvar

-0.371429AdjustedR-squared0.999683S.D.dependentvar5.992507S.E.ofregression0.106754Akaikeinfocriterion-1.432327Sumsquaredresid0.182342Schwarzcriterion-1.183631Loglikelihood20.03943Hannan-Quinncriter.-1.378354F-statistic15751.09Durbin-Watsonstat2.05844Prob(F-statistic)0.000000由上表的统计结果可知：DW=2.05844，查表得DL=0.93DU=1.81，DU<DW<4-DU，所以解释变量之间无自相关的T统计结果是-0.540527，其确定值小于T0.025〔16〕=2.120，且其系数符号与预期相反，这说明可能存在严峻的多重共线性。④多重共线性检验计算各解释变量的相关系数：DX1DX2DY10.178708028002DY20.1846143580471DX110.856702273863038806864240.85670227386300.003445225105-0.0180020231662DX2388136754116860.17870802800200.00344522510536750.9308105035541DY1686411423DY2

0.1846143580471-0.0180020231662160.930810503554424 86 1423 1变量R2变量R2Dy10.955376Dy20.973749DX10.34607DX20.000045R2由大到小排序为：dy2、dy1、dx1、dx2首先参与dy1DependentVariable:DYMethod:LeastSquaresDate:12/22/10 Time:14:03Sample(adjusted):19862023Includedobservations:21afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C0.0058130.0224950.2584170.7990DY20.5713840.01037255.087130.0000DY10.4265990.01011842.163490.0000R-squared 0.999737 Meandependent -0.3714Adjusted

varS.D.dependent0.999708varAkaikeinfo

295.992507-1.5870S.E.ofregression0.102464criterionSumsquaredresid 0.188980 SchwarzcriterionHannan-QuinnLoglikelihood 19.66397criter.Durbin-WatsonF-statistic 34194.74statProb(F-statistic) 0.000000

45-1.437827-1.5546612.381670dy1的T检验结果显著，不予剔除R2=0.999737 F=34194.74 DW=2.381670的T检验结果显著，且拟合程度也好，消退多重共线性参与dx1DependentVariable:DYMethod:LeastSquaresDate:12/22/10 Time:14:17Sample(adjusted):19862023Includedobservations:21afteradjustmentsCoefficieVariablentStd.Errort-StatisticProb.-0.12438C50.425416-0.2923830.7733DY20.9777310.03851825.384020.0000DX18.0908950.0007880.1027080.9193Meandependent -0.3714Adjusted

0.973764varS.D.dependent0.970849varAkaikeinfo

295.9925073.01518S.E.ofregression1.023134criterionSumsquaredresid 18.84245 Schwarzcriterion-28.6594 Hannan-QuinnLoglikelihood 0criter.Durbin-WatsonF-statistic 334.0460statProb(F-statistic) 0.000000

13.1643993.0475652.053324Y=-0.124385+0.977731dy2+8.090895dx1dx1的T检验结果不显著，予以剔除参与dx2DependentVariable:DYMethod:LeastSquaresDate:12/22/10 Time:14:21Sample(adjusted):19862023Includedobservations:21afteradjustmentsCoefficieVariablentStd.Errort-StatisticProb.-0.16672C30.353366-0.4718150.6427DY20.9786580.03778425.901080.0000DX20.0006690.0023050.2901480.7750Meandependent -0.3714Adjusted

0.9