我国国内生产总值(GDP)影响因素的回归分析

1、精选文档我国国内生产总值（GDP）影响因素的实证分析摘要：本文以国民收入核算理论为基础，引入能源消费、就业人数、居民消费水平、社会消费品零售总额、进出口贸易总额、外商直接投资（FDI）等解释变量，运用计量经济学分析的方法，分析国内生产总值与这些解释变量之间的关系。从中国的实际情况出发，在利用从1985到2001的年度时间序列数据分析的基础上，分析各因素对国内生产总值的不同程度的影响及其原因，最后得出结论、提出观点。关键词：GDP 影响因素 实证分析 Eviews一、问题提出：国内生产总值（GDP）是指在一定时期内(一个季度或一年)，一个国家或地区的经济中所生产出的全部最终产品和劳务的价值，常被

2、公认为衡量国家经济状况的最佳指标。它不但可反映一个国家的经济表现，更可以反映一国的国力与财富。国民经济，作为一个复杂的综合体，它的影响因素一直是人们探索和争论的热点，根据西方经济学中关于国民收入核算的经典理论，我们建立以GDP为被解释变量的线性回归模型，引入能源消费、就业人数、居民消费水平、社会消费品零售总额、进出口贸易总额以及外商直接投资等解释变量，对GDP的影响因素作实证分析，试图揭示这几个解释变量对GDP的影响程度。二、样本数据选取及模型设定：回归模型设立如下：Y =0 +1 X1 +2 X2+3 X3+4 X4 +5 X5+6 X6+UY-国内生产总值GDP X1-能源消费X2-就业人

3、数X3-居民消费水平X4-社会消费品零售总额X5-进出口贸易总额X6-外商直接投资（FDI）U-随机扰动项1、2、3、4、5、6为待估参数。变量采用时间序列数据，具体数据(现价计算)见表一：表1：年份GDPX1能源消费总量（万吨标准煤）X2就业人员（万人）X3居民消费水平（元）X4社会消费品零售总额（亿元）X5进出口贸易总额（亿元）X6外商直接投资（FDI）19858964.4766824987343743052066.748. 69198610202.2808505128244749502850.464. 71198711962.5866325278350858203084.286. 131

4、98814928.3929975433463574403822118. 88198916909.296934553297628101.44155.9127. 71199018547.998703567408038300.15560.1166. 79199121617.8103783583608969415.67229.3232. 42199226638.110917059432107010993.79119.6606. 99199334634.411599360220133112462.1112711585. 41199446759.412273761470174616264.720381.9

5、2910. 28199558478.11311766238822362062023499.93133. 38199667884.613894868850264124774.124133.83469. 1199774462.613817369600283427298.926967.23751. 71199878345.213221470637297229152.526849.73763. 93199982067.513011971394313831134.729896.23337. 73200089442.213029772085339734152.639273.23370. 552001959

6、33.313491473025360937595.242183.63880. 09（数据来源于中国统计年鉴。）三、参数的初步估计与检验将第一个模型的样本导入Eviews软件进行OLS估计，得到输出结果如下：表2：Dependent Variable: YMethod: Least SquaresDate: 06/17/12 Time: 15:27Sample: 1985 2001Included observations: 17VariableCoefficientStd. Errort-StatisticProb.  C-13277.729457.897-1.403876

7、0.1939X10.0527430.0507771.0387200.3260X20.1338460.2417630.5536240.5933X318.576204.2627744.3577740.0018X40.3772430.4597350.8205670.4331X50.1697060.6896380.2460790.8111X60.0022220.0009742.2801980.0485R-squared0.999741    Mean dependent var44575.16Adjusted R-squared0.999539 

8、60;  S.D. dependent var31239.02S.E. of regression670.8163    Akaike info criterion16.16006Sum squared resid4049950.    Schwarz criterion16.55216Log likelihood-129.3605    F-statistic4955.607Durbin-Watson stat1.833054  &#

9、160; Prob(F-statistic)0.000000将上述回归结果整理如下： =-13277.72+0.052743X10.133846X218.57620X30.377243X40.169706X50.002222X6 0.999741， 0.999539， F=4955.607从回归结果看，可决系数很高，F值很大，但在显著性水平下，很多项的回归系数都不显著，因此回归方程不能投入使用；该模型很可能存在多重共线性。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，而t值小于临界值恰好反映了由于解释变量共线性的作用，使得不能分解出各个解释变量对Y独立影响。1.模型检验：(1)

10、经济意义检验由回归估计结果可以看出，能源消费、就业人数、居民消费水平、社会消费品零售总额、进出口贸易总额以及FDI与GDP线性正相关，这与现实中GDP随能源消费、就业人数、居民消费水平、社会消费品零售总额、进出口贸易总额以及FDI的增加而增长是相符的。(2)统计推断检验从估计的结果可以看出，可决系数R2=0.999741，F统计量=4955.607，表明模型在整体上拟合地比较理想。系数显著性检验：给定=0.05，X3、X6的t的P值小于给定的显著性水平，表明居民消费水平、FDI对GDP有显著性影响。2.计量经济学检验(1)多重共线性的检验 用Eviews计算解释变量之间的简单相关系数：表3：X

11、1X2X3X4X5X6X11.0000000.9426950.9183470.8946260.8877610.945640X20.9426951.0000000.9806900.9776240.9521690.930263X30.9183470.9806901.0000000.9968550.9851270.958071X40.8946260.9776240.9968551.0000000.9866140.936663X50.8877610.9521690.9851270.9866141.0000000.938836X60.9456400.9302630.9580710.9366630.938

12、8361.000000由此可见，模型存在严重的多重共线。模型修正:运用OLS方法逐一求Y对各个解释变量的回归，结合经济意义和统计检验选出拟合效果最好的一元线性回归方程。过程如下：表4：Dependent Variable: YMethod: Least SquaresDate: 06/17/12 Time: 17:08Sample: 1985 2001Included observations: 17VariableCoefficientStd. Errort-StatisticProb. X11.3543760.1497619.0435770.0000C-108415.317195.21-6

13、.3049670.0000R-squared0.845019 Mean dependent var44575.16Adjusted R-squared0.834687 S.D. dependent var31239.02S.E. of regression12701.37 Akaike info criterion21.84694Sum squared resid2.42E+09 Schwarz criterion21.94496Log likelihood-183.6990 F-statistic81.78629Durbin-Watson stat0.176301 Prob(F-statis

14、tic)0.000000表5：Dependent Variable: YMethod: Least SquaresDate: 06/17/12 Time: 17:09Sample: 1985 2001Included observations: 17VariableCoefficientStd. Errort-StatisticProb. X23.8902240.20673518.817480.0000C-195200.412839.45-15.203180.0000R-squared0.959360 Mean dependent var44575.16Adjusted R-squared0.

15、956651 S.D. dependent var31239.02S.E. of regression6504.094 Akaike info criterion20.50838Sum squared resid6.35E+08 Schwarz criterion20.60641Log likelihood-172.3212 F-statistic354.0975Durbin-Watson stat0.801484 Prob(F-statistic)0.000000表6：Dependent Variable: YMethod: Least SquaresDate: 06/17/12 Time:

16、 17:11Sample: 1985 2001Included observations: 17VariableCoefficientStd. Errort-StatisticProb. X327.106670.178293152.03420.0000C-2402.301367.6774-6.5337200.0000R-squared0.999351 Mean dependent var44575.16Adjusted R-squared0.999308 S.D. dependent var31239.02S.E. of regression821.6280 Akaike info crite

17、rion16.37058Sum squared resid10126089 Schwarz criterion16.46861Log likelihood-137.1500 F-statistic23114.40Durbin-Watson stat1.345961 Prob(F-statistic)0.000000表7：Dependent Variable: YMethod: Least SquaresDate: 06/17/12 Time: 17:10Sample: 1985 2001Included observations: 17VariableCoefficientStd. Error

18、t-StatisticProb. X42.7586460.06065445.482060.0000C-2935.3201237.736-2.3715220.0315R-squared0.992801 Mean dependent var44575.16Adjusted R-squared0.992321 S.D. dependent var31239.02S.E. of regression2737.464 Akaike info criterion18.77758Sum squared resid1.12E+08 Schwarz criterion18.87561Log likelihood

19、-157.6094 F-statistic2068.618Durbin-Watson stat0.372005 Prob(F-statistic)0.000000表8：Dependent Variable: YMethod: Least SquaresDate: 06/17/12 Time: 17:12Sample: 1985 2001Included observations: 17VariableCoefficientStd. Errort-StatisticProb. X52.3014710.09675423.786840.0000C6351.1542040.4923.1125600.0

20、071R-squared0.974174 Mean dependent var44575.16Adjusted R-squared0.972452 S.D. dependent var31239.02S.E. of regression5184.883 Akaike info criterion20.05501Sum squared resid4.03E+08 Schwarz criterion20.15304Log likelihood-168.4676 F-statistic565.8137Durbin-Watson stat1.015958 Prob(F-statistic)0.0000

21、00表9：Dependent Variable: YMethod: Least SquaresDate: 06/17/12 Time: 17:20Sample: 1985 2001Included observations: 17VariableCoefficientStd. Errort-StatisticProb. X618.139351.34119913.524730.0000C11876.313238.2063.6675590.0023R-squared0.924211 Mean dependent var44575.16Adjusted R-squared0.919159 S.D.

22、dependent var31239.02S.E. of regression8882.076 Akaike info criterion21.13159Sum squared resid1.18E+09 Schwarz criterion21.22961Log likelihood-177.6185 F-statistic182.9182Durbin-Watson stat0.444848 Prob(F-statistic)0.000000从以上一系列表的回归结果中看出，仅表六中X3与Y的拟合优度最大，达到0.999351，接近于1，所以只保留X3变量。 (2)异方差检验e2和x3散点图：由

23、图可见，可能存在异方差。GoldfieldQuanadt检验：样本容量n17，删除中间1/4的观测值，大约4个观测值，余下不奉分为两个样本区间：19851990，19962001样本数均为6个，即n1n26。OLS方法求得一下结果：表10：Dependent Variable: YMethod: Least SquaresDate: 06/18/12 Time: 14:12Sample: 1985 1990Included observations: 6VariableCoefficientStd. Errort-StatisticProb. X323.704061.71213513.8447

24、30.0002C-605.07841054.857-0.5736120.5969R-squared0.979558 Mean dependent var13585.75Adjusted R-squared0.974448 S.D. dependent var3818.663S.E. of regression610.4173 Akaike info criterion15.92736Sum squared resid1490437. Schwarz criterion15.85795Log likelihood-45.78209 F-statistic191.6766Durbin-Watson

25、 stat1.749082 Prob(F-statistic)0.000158表11：Dependent Variable: YMethod: Least SquaresDate: 06/18/12 Time: 14:16Sample: 1996 2001Included observations: 6VariableCoefficientStd. Errort-StatisticProb. X328.228480.69755840.467570.0000C-6110.0402173.479-2.8111790.0483R-squared0.997563 Mean dependent var81355.90Adjusted R-squared0.996954 S.D. dependent var10163.20S.E. of regression560.891