我国利用外资与GDP关系

1、我国利用外资与gdp关系一、导论本模型是研究近二十年来我国的利用外资额与gdp之间的定量关系。gdp即国内生产总值，利用外资分为实际利用外资额和计划利用外资额两类。我们分析所指的是实际利用外资额。经过对国家改革开放之后经济数据的研究，我们发现外资的利用与我国gdp的增长有着密切的关系，为此我们建立如下计量经济模型：y=c1+c2*x+u这里y是被解释变量gdp，x是解释变量实际利用外资额，c2可以看作实际利用外资额对gdp的平均影响，且0c22.1315,所以拒绝原假设c2=0,表明我国实际每年平均利用外资额对年均gdp有显著性影响.四 计量经济检验(一) 作3期滞后arch检验arch te

2、st:f-statistic0.198726 probability0.894654obs*r-squared0.807645 probability0.847638test equation:dependent variable: resid2method: least squaresdate: 05/16/04 time: 14:36sample(adjusted): 1989 2001included observations: 13 after adjusting endpointsvariablecoefficientstd. errort-statisticprob. c3.11e

3、+111.83e+111.6970030.1239resid2(-1)0.2349780.3249250.7231770.4879resid2(-2)-0.1398890.338580-0.4131630.6892resid2(-3)0.0835770.3149860.2653350.7967r-squared0.062127 mean dependent var3.72e+11adjusted r-squared-0.250498 s.d. dependent var3.23e+11s.e. of regression3.61e+11 akaike info criterion56.3111

4、8sum squared resid1.17e+24 schwarz criterion56.48501log likelihood-362.0227 f-statistic0.198726durbin-watson stat2.084996 prob(f-statistic)0.894654从图中可知道,obs* r-squared=0.807645小于给定的(自由度为3, =0.05)x2(注:音 卡方)分布值,接受原假设.表明模型中随机误差项不存在异方差.(二)自相关的检验利用图示法,我们得到了以下的图表从图中可以看出残差e呈线性自回归,表明随机误差项u存在自相关.(三) 自相关的修正(

5、1).广义差分法由dw=0.462140,根据=1-dw/2得=0.76893.然后我们用genr生成了dy和dx,并对其进行ols估计,dependent variable: dymethod: least squaresdate: 05/16/04 time: 16:01sample(adjusted): 1987 2001included observations: 15 after adjusting endpointsvariablecoefficientstd. errort-statisticprob. dx0.0106280.0025354.1933560.0011c7003.

6、3652567.5462.7276490.0173r-squared0.574944 mean dependent var15769.80adjusted r-squared0.542248 s.d. dependent var8532.768s.e. of regression5773.048 akaike info criterion20.28335sum squared resid4.33e+08 schwarz criterion20.37776log likelihood-150.1252 f-statistic17.58423durbin-watson stat0.757391 p

7、rob(f-statistic)0.001053由该图我们可以得到: dy = 0.01062807325*dx + 7003.364539t=(4.193356) (2.727649)r-squared=0.574944,f=17.58423,dw=0.757391这时,我们发现经过广义差分法,dw值有所提高,但dw=0.757391小于下限临界值1.077(上限临界值为1.361),所以仍存在正的一阶自相关.(2)cochrane-orcutt迭代法dependent variable: ymethod: least squaresdate: 05/16/04 time: 16:16sam

8、ple(adjusted): 1987 2001included observations: 15 after adjusting endpointsconvergence not achieved after 100 iterationsvariablecoefficientstd. errort-statisticprob. c7964420.5.26e+080.0151320.9882x0.0037420.0017732.1101100.0565ar(1)0.9994200.03868225.836950.0000r-squared0.990351 mean dependent var4

9、9227.75adjusted r-squared0.988743 s.d. dependent var30266.14s.e. of regression3211.177 akaike info criterion19.16352sum squared resid1.24e+08 schwarz criterion19.30513log likelihood-140.7264 f-statistic615.8472durbin-watson stat0.791191 prob(f-statistic)0.000000inverted ar roots 1.00从表中可以看出dw=0.7911

10、91,仍存在自相关.(3)利用对数线性回归修正自相关.dependent variable: lymethod: least squaresdate: 05/16/04 time: 16:25sample: 1986 2001included observations: 16variablecoefficientstd. errort-statisticprob. c1.9975540.5916323.3763440.0045lx0.6005660.04165014.419190.0000r-squared0.936912 mean dependent var10.49662adjusted

11、r-squared0.932406 s.d. dependent var0.785034s.e. of regression0.204100 akaike info criterion-0.223945sum squared resid0.583195 schwarz criterion-0.127372log likelihood3.791563 f-statistic207.9130durbin-watson stat0.559697 prob(f-statistic)0.000000同时考虑cochrane-orcutt迭代得下表:dependent variable: lymethod

12、: least squaresdate: 05/16/04 time: 16:27sample(adjusted): 1987 2001included observations: 15 after adjusting endpointsconvergence achieved after 10 iterationsvariablecoefficientstd. errort-statisticprob. c11.095511.8355706.0447210.0001lx0.1426930.0548402.6019750.0231ar(1)0.9549020.02747634.753840.0

13、000r-squared0.994174 mean dependent var10.58104adjusted r-squared0.993203 s.d. dependent var0.733575s.e. of regression0.060481 akaike info criterion-2.596129sum squared resid0.043895 schwarz criterion-2.454519log likelihood22.47096 f-statistic1023.802durbin-watson stat0.976748 prob(f-statistic)0.000000inverted ar roots .95从估计得结果看来,dw=0.976748与以上其他dw值相比明显好转.五. 总结通过以上分析，我们得到如下方程：ly = 11.09550954 +