wj fx jcy 计量经济学论文__影响国内生产总值(GDP)因素

1、计量经济学论文影响国内生产总值（GDP）的因素一、研究的问题摘要：经济增长是世界各国永恒的主题，不论是在发达国家还是在发展中国家，都处于一个极其重要的地位。近年来，我国国内生产总值（GDP）逐年飞速增长，经济发展令世人瞩目。国内生产总值（GDP）是宏观经济中最受关注的经济统计数字，被公认为是衡量国民经济发展情况最重要的一个指标。GDP是指本国在一年内所生产创造的劳动产品及劳务的总价值。GDP 的增长对于一个国家有着十分重要的意义，它衡量一国在过去的一年里所创造的劳动成果。关键词：国内生产总值（GDP）、社会总投资、劳动力、新古典模型、经济增长引言：影响国内生产总值的因素有很多，但在国外，已有大

2、量的经验事实和研究表明社会总资本和劳动力是促进经济持续增长的最主要的动力因素。所以本论文只从供求方面来探究经济增长的影响要素 。研究它的影响因素不仅可以很好的了解GDP的经济内涵，而且还有利于我们根据这些因素对GDP影响大小制定更好的政策来促进国民经济的发展， 试图揭示这几个解释变量对GDP的影响程度。为此拟通过回归分析来测量社会总资本和劳动力对经济增长的产出弹性。二、模型设定 采用国内生产总值GDP（为被解释变量，用变量Y表示）衡量经济增长；劳动力（为解释变量，用变量L表示）用统计年鉴上公布的历年的全社会就业总人数来近似替代；物质资本投入量（为解释变量，用变量K表示）用全社会固定资产投资来近

3、似衡量。下表为在中国统计年鉴（2010）中得到的数据： 1990-2009年中国的国内生产总值、社会总资本及劳动力总量数据表年 份国内生产总值GDP（Y）/亿元社会总投资（K）/亿元劳动力（L）/万人lnYlnKlnL199018667.84517647499.8345578.41560311.07827199121781.55594.5654919.9888168.62953911.08967199226923.58080.16615210.200758.9971611.09971199335333.913072.36680810.47269.47825111.10958199448197.

4、917042.96745510.783079.74348911.11922199560793.720019.36806511.015249.90445211.12822199671176.622913.56895011.1729210.0394811.14114199778973.024941.16982011.2768610.1242711.15368199884402.328406.27063711.3433510.2543611.16531199989677.129854.77139411.4039710.304111.17597200099214.632917.77208511.505

5、0410.4017711.18562001109655.237213.57302511.605110.5244311.198562002120332.743499.97374011.6980210.6805111.20832003135822.855566.67443211.8191110.9253411.217642004159878.370477.47520011.9821711.1630511.227912005184937.488773.67582512.1277711.3938411.236182006216314.4109998.27640012.2844911.6082211.2

6、43742007265810.3137323.97699012.4905411.830111.251432008314045.4172828.47748012.6572912.0600511.257782009340506.9224598.87799512.7381912.3220711.2644前4列数据来自中国统计年鉴（2010），后3列数据是利用Excel软件求得的数据借用新古典经济增长模型，设置经济增长总量生产函数为： Y=AK1L2E等式两边同时取对数，得到：lnY =lnA+1lnK +2lnL +lnA，1，2为待估计参数三、估计参数假定随机扰动项满足古典假定，可用最小二乘估计法

7、（OLS法）估计其参数，运用Eviews软件，将以上数据导入，进行参数估计，得到下表：Dependent Variable: LNYMethod: Least SquaresDate: 05/14/11 Time: 23:12Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb. C-33.7585515.83522-2.1318650.0479LNK0.5893410.0823067.1603770.0000LNL3.4914261.4918152.3403880.0317

8、R-squared0.991956 Mean dependent var11.41999Adjusted R-squared0.991009 S.D. dependent var0.854098S.E. of regression0.080985 Akaike info criterion-2.051622Sum squared resid0.111496 Schwarz criterion-1.902262Log likelihood23.51622 F-statistic1048.144Durbin-Watson stat0.451321 Prob(F-statistic)0.000000

9、Dependent Variable: LNYMethod: Least SquaresDate: 05/27/14 Time: 15:29Sample: 1990 2009Included observations: 20CoefficientStd. Errort-StatisticProb.  C-33.7655015.83613-2.1321810.0479LNK0.5893060.0823117.1595330.0000LNL3.4920811.4919012.3406920.0317R-squared0.991956    

10、;Mean dependent var11.41999Adjusted R-squared0.991010    S.D. dependent var0.854098S.E. of regression0.080983    Akaike info criterion-2.051675Sum squared resid0.111490    Schwarz criterion-1.902316Log likelihood23.51675   &#

11、160;Hannan-Quinn criter.-2.022519F-statistic1048.201    Durbin-Watson stat0.451278Prob(F-statistic)0.000000根据表中的数据，模型估计的结果为：LNY = -33.75854925 + 0.5893412851*LNK + 3.491426195*LNLSE= (15.83522) (0.082306) (1.491815)t= (-2.131865) (7.160377) (2.340388)R2=0.991956 0.991009 F=1048.1

12、44 DW=0.451321t0.025（17）=2.110 F0.05（2，17）=3.59从回归结果看，可决系数R2很高，F值很大，而且在显著性水平下，回归系数都较为显著，看似回归方程可以投入使用。和F值大反映了模型中各解释变量联合对Y的影响力显著。 四、模型检验1.经济意义检验模型估计结果，LNK的系数为0.589341，说明社会总资本投资每增加一个单位就可以拉动0.589341的经济增长。LNL的系数为3.491426，说明劳动力投入每增加一个单位就可以拉动3.491426的经济增长。这符合我国经济增长的实际情况。改革开放以来依靠人口红利的优势以及大力引进外资，我国经济增长发生了翻天覆

13、地的变化。2.拟合优度和统计检验R2为0.991956，修正后的 R2为0.991009，说明经济增长几乎都是由资本和劳动力来拉动的。社会总资本和劳动力的联合检验的 P 值为0亦说明社会总资本和劳动力共同促进经济增长。模型对样本的拟合很好。F检验：针对H0：1=2=0，给定显著性水平=0.05，查表得F0.05（2，17）=3.59，F=1048.144远远大于F0.05（2，17）=3.59，应拒绝原假设，说明回归方程显著，即社会总投资与劳动力对国内生产总值（GDP）有显著影响T检验：从模型结果可以看出，社会总资本和劳动力都通过了各自的 T 检验，相对应的 P 值分别 0 和0.0317，这

14、说明资本和劳动力对我国经济增长的促进作用是直接而且非常显著。3.多重共线性检验LNYLNKLNLLNY10.99466762085140060.9837184015364077LNK0.994667620851400610.9784135879779729LNL0.98371840153640770.97841358797797291上图表明，解释变量之间的相关系数很高，证实确实存在多重共线性。以lnK为被解释变量，lnL为解释变量得到下表。各值均有显著效果，所以也说明存在多重共线性。Dependent Variable: LNKMethod: Least SquaresDate: 05/19

15、/11 Time: 17:17Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb. C-187.78309.869342-19.026900.0000LNL17.733930.88294420.085000.0000R-squared0.957286 Mean dependent var10.44000Adjusted R-squared0.954913 S.D. dependent var1.092225S.E. of regression0.231920 Akaike

16、info criterion0.009790Sum squared resid0.968163 Schwarz criterion0.109363Log likelihood1.902104 F-statistic403.4072Durbin-Watson stat0.282620 Prob(F-statistic)0.000000Dependent Variable: LNKMethod: Least SquaresDate: 05/27/14 Time: 16:01Sample: 1990 2009Included observations: 20CoefficientStd. Error

17、t-StatisticProb.  C-187.78389.868515-19.028580.0000LNL17.734000.88287020.086760.0000R-squared0.957293    Mean dependent var10.44000Adjusted R-squared0.954921    S.D. dependent var1.092225S.E. of regression0.231900    Akaike info c

18、riterion0.009621Sum squared resid0.967999    Schwarz criterion0.109194Log likelihood1.903790    Hannan-Quinn criter.0.029059F-statistic403.4780    Durbin-Watson stat0.282666Prob(F-statistic)0.000000补救：利用逐步回归法进行多重共线性的补救。首先做lnY与lnK的一元回归方程，得到下

19、表：Dependent Variable: LNYMethod: Least SquaresDate: 05/23/11 Time: 12:31Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb. C3.2996510.19948016.541230.0000LNK0.7778100.01900940.918570.0000R-squared0.989364 Mean dependent var11.41999Adjusted R-squared0.988773 S.D.

20、dependent var0.854098S.E. of regression0.090499 Akaike info criterion-1.872324Sum squared resid0.147420 Schwarz criterion-1.772751Log likelihood20.72324 F-statistic1674.329Durbin-Watson stat0.465767 Prob(F-statistic)0.000000得到修正的R2=0.988773。再做lnY与lnL的一元回归方程，得到下表：Dependent Variable: LNYMethod: Least

21、SquaresDate: 05/23/11 Time: 12:31Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb. C-144.42686.711774-21.518430.0000LNL13.942760.60045723.220230.0000R-squared0.967694 Mean dependent var11.41999Adjusted R-squared0.965900 S.D. dependent var0.854098S.E. of regressi

22、on0.157720 Akaike info criterion-0.761350Sum squared resid0.447761 Schwarz criterion-0.661777Log likelihood9.613503 F-statistic539.1793Durbin-Watson stat0.262494 Prob(F-statistic)0.000000Dependent Variable: LNYMethod: Least SquaresDate: 05/27/14 Time: 16:04Sample: 1990 2009Included observations: 20C

23、oefficientStd. Errort-StatisticProb.  C3.2996460.19948116.541130.0000LNK0.7778110.01900940.918380.0000R-squared0.989364    Mean dependent var11.41999Adjusted R-squared0.988773    S.D. dependent var0.854098S.E. of regression0.090499   &

24、#160;Akaike info criterion-1.872314Sum squared resid0.147422    Schwarz criterion-1.772741Log likelihood20.72314    Hannan-Quinn criter.-1.852877F-statistic1674.314    Durbin-Watson stat0.465759Prob(F-statistic)0.000000得到修正的R2=0.965900，所以保留

25、lnK。再进行逐步回归，发现两个解释变量的共同的修正的R2为0.991009，大于保留下来的lnK的修正的R2（0.965900），所以两个解释变量均保留。理论也表明这两个解释变量对模型确实有影响，符合经济意义，所以两个解释变量均不能剔除，一切以理论为依据。4.异方差检验运用Eviews软件做White检验，得到下表：White Heteroskedasticity Test:F-statistic2.384089 Probability0.097687Obs*R-squared7.773246 Probability0.100247Test Equation:Dependent Variab

26、le: RESID2Method: Least SquaresDate: 05/14/11 Time: 23:14Sample: 1990 2009Included observations: 20VariableCoefficientStd. Errort-StatisticProb. C-11.4911220.19133-0.5691120.5777LNK1.3148341.8765880.7006520.4942LNK20.0050440.0086770.5813450.5696LNK*LNL-0.1260110.183873-0.6853150.5036LNL1.0670241.889

27、4420.5647300.5806R-squared0.388662 Mean dependent var0.005575Adjusted R-squared0.225639 S.D. dependent var0.005633S.E. of regression0.004957 Akaike info criterion-7.563643Sum squared resid0.000369 Schwarz criterion-7.314710Log likelihood80.63643 F-statistic2.384089Durbin-Watson stat2.217457 Prob(F-s

28、tatistic)0.097687Heteroskedasticity Test: WhiteF-statistic2.385027    Prob. F(4,15)0.0976Obs*R-squared7.775115    Prob. Chi-Square(4)0.1002Scaled explained SS2.724439    Prob. Chi-Square(4)0.6049Test Equation:Dependent Variable: RESID2Metho

29、d: Least SquaresDate: 05/27/14 Time: 16:23Sample: 1990 2009Included observations: 20Collinear test regressors dropped from specificationCoefficientStd. Errort-StatisticProb.  C-11.4934220.18846-0.5693060.5776LNK1.3150641.8763150.7008760.4941LNK20.0050450.0086760.5815270.5695LNK*LNL-0.12603

30、30.183847-0.6855340.5035LNL1.0672381.8891750.5649230.5805R-squared0.388756    Mean dependent var0.005575Adjusted R-squared0.225757    S.D. dependent var0.005633S.E. of regression0.004956    Akaike info criterion-7.563967Sum squared resid0.0

31、00368    Schwarz criterion-7.315034Log likelihood80.63967    Hannan-Quinn criter.-7.515373F-statistic2.385027    Durbin-Watson stat2.217427Prob(F-statistic)0.097593得到数据nR2=7.773246小于=9.48773，所以此模型不存在异方差。5.自相关检验k=2，n=20，查表得到dL=1.100， dU=1.53

32、7，DW=0.451321小于 dL=1.100，说明存在一阶正相关:，也可从下面的残差图中看出，表明t统计量和F统计量的结论不可信，需要采取补救措施。通过偏相关检验（如图）观察存在二阶自相关。做滞后两期的自回归得到回归方程 E = 1.217452785*E(-1) - 0.5445394461*E(-2)在命令栏里输入：ls lny-1.217453*lny(-1)+0.544539*lny(-2) c lnk-1.217453*lnk(-1)+0.544539*lnk(-2) lnl-1.217453*lnl(-1)+0.544539*lnl(-2)得到下表：Dependent Vari

33、able: LNY-1.217453*LNY(-1)+0.544539*LNY(-2)Method: Least SquaresDate: 06/10/11 Time: 21:57Sample(adjusted): 1992 2009Included observations: 18 after adjusting endpointsVariableCoefficientStd. Errort-StatisticProb. C-29.583206.676299-4.4310780.0005LNK-1.217453*LNK(-1)+0.544539*LNK(-2)0.2311450.1101542.2983780.0432LNL-1.217453*LNL(-1)+0.544539*LNL(-2)8.9000071.9258314.6213850.0003R-squared0.978451 Mean dependent var3.807525Adjusted R-squared0.975577 S.D. dependent var0.2264