李子奈版计量经济学作业
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计量经济学P136, T8
中国1980-2000年投资总额X与工业总产值Y的统计资料如表所示,试问:
(1) 当设定模型为01lnlntttYX时,是否存在序列关?
(2) 若按一阶自相关假设1ttt,试用杜宾两步法与广义最小二乘法估计原模型。
(3) 采用差分形式**1ttt1YYYtttXXX与作为新数据,估计模型**01tttYX,该模型是否存在序列相关?
年份 固定资产投资X 工业增加值Y
1980 910.9 1996.5
1981 961 2048.4
1982 1230.4 2162.3
1983 1430.1 2375.6
1984 1832.9 2789
1985 2543.2 3448.7
1986 3120.6 3967
1987 3791.7 4585.8
1988 4753.8 5777.2
1989 4410.4 6484 1990 4517 6858
1991 5594.5 8087.1
1992 8080.1 10284.5
1993 13072.3 14143.8
1994 17042.1 19359.6
1995 20019.3 24718.3
1996 22913.5 29082.6
1997 24941.1 32412.1
1998 28406.2 33387.9
1999 29854.7 35087.2
2000 32917.7 39570.3
答:
(1)先用最小二乘法回归,得到以下回归结果。
Dependent Variable: LOG(Y)
Method: Least Squares
Date: 04/13/09 Time: 20:55
Sample: 1980 2000
Included observations: 21
Variable Coefficient Std. Error t-Statistic Prob.
C 1.452109 0.190925 7.605641 0.0000
LOG(X) 0.870419 0.021727 40.06186 0.0000
R-squared 0.988300 Mean dependent var 9.031179
Adjusted R-squared 0.987684 S.D. dependent var 1.062296
S.E. of regression 0.117889 Akaike info criterion -1.347752
Sum squared resid 0.264059 Schwarz criterion -1.248273
Log likelihood 16.15139 F-statistic 1604.952
Durbin-Watson stat 0.451709 Prob(F-statistic) 0.000000
得到如下方程:
Ln(Y) = 1.45210911 + 0.8704188768*Ln(X)
进行序列相关性检验
1) 残差图分析:
点击EViews方程输出窗口的按钮Resids可得到残差图 计量经济学P136, T8
在残差图中,残差的变动有系统模式,连续为正和连续为负,表明残差项存在正自相关,模型中t统计量和F统计量的结论不可信,需采取补救措施。
2) D-W检验:由于D-W值为0.45 ,从D-W表中可以看到,对于n=21,k=1,在5%的显著水平下:ld1.20 ud1.41 ,而00.451.221lDWd,得出回归方程的残差存在正的自相关。
(2)
采用杜宾两步法估计
第一步,估计模型
101110111lnln(1)(lnln)lnln(1)lnlnttttttttYYXXYYXX
11ln0.6319ln0.44560.4704ln0.1322lnttttYYXX
第二步,将估计的ˆ=0.6319代入差分模型,得到广义差分模型,进行OLS估计
1011lnln(1)(lnln)ttttYYXX
Dependent Variable: LOG(Y)-0.6319*LOG(Y(-1))
Date: 04/13/09 Time: 21:21
Sample (adjusted): 1981 2000
Included observations: 20 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
C 0.415321 0.127908 3.247035 0.0045
LOG(X)-0.6319*LOG(X(-1)) 0.903502 0.037849 23.87093 0.0000
R-squared 0.969378 Mean dependent var 3.445098
Adjusted R-squared 0.967677 S.D. dependent var 0.394005
S.E. of regression 0.070836 Akaike info criterion -2.362251
Sum squared resid 0.090320 Schwarz criterion -2.262678 Log likelihood 25.62251 F-statistic 569.8215
Durbin-Watson stat 1.333248 Prob(F-statistic) 0.000000
-.2-.1.0.1.2.378910118082848688909294969800ResidualActualFitted-3000-2000-10000100020003000-3000-2000-10000100020003000E(t-1)Et计量经济学P136, T8
11ln0.6319ln0.41530.9035(ln0.6319ln)ttttYYXX
(3.25) (23.87)
R2=0.9694 F=569.82 D.W.=1.333
在5%显著性水平下,n=20的DW临界值上界和下界分别为1.2和1.41,故无法判断。
由于 ,于是原模型估计式为:
0.4153ln0.9035ln10.6319ln1.12820.9035lnttttYXYX
采用柯克伦-奥科特迭代法估计
在Eview软件包下,一阶广义差分模型估计结果为:
Ln Y= 1.126212317 + 0.903538342LnX + 0.6495609992 AR(1)
Dependent Variable: LOG(Y)
Method: Least Squares
Date: 04/13/09 Time: 21:58
Sample (adjusted): 1981 2000
Included observations: 20 after adjustments
Convergence achieved after 21 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C 1.126212 0.414195 2.719042 0.0146
LOG(X) 0.903538 0.044383 20.35794 0.0000
AR(1) 0.649561 0.145680 4.458834 0.0003
R-squared 0.995581 Mean dependent var 9.102781
Adjusted R-squared 0.995061 S.D. dependent var 1.036599
S.E. of regression 0.072853 Akaike info criterion -2.263272
Sum squared resid 0.090228 Schwarz criterion -2.113912
Log likelihood 25.63272 F-statistic 1914.826
Durbin-Watson stat 1.348353 Prob(F-statistic) 0.000000
Inverted AR Roots .65
(3)记**1ttt1YYYtttXXX ,,直接差分法估计结果如下表
Dependent Variable: D(Y)
Method: Least Squares
Date: 04/13/09 Time: 22:57
Sample (adjusted): 1981 2000
Included observations: 20 after adjustments
1*10*0 ),1(计量经济学P136, T8
Variable Coefficient Std. Error t-Statistic Prob.
C 291.0354 350.1038 0.831283 0.4167
D(X) 0.992073 0.160235 6.191368 0.0000
R-squared 0.680472 Mean dependent var 1878.690
Adjusted R-squared 0.662720 S.D. dependent var 1835.506
S.E. of regression 1065.985 Akaike info criterion 16.87582
Sum squared resid 20453815 Schwarz criterion 16.97540
Log likelihood -166.7582 F-statistic 38.33304
Durbin-Watson stat 1.620069 Prob(F-statistic) 0.000008
对于n=21,k=1,在5%的显著水平下:ud1.41,可以判断已经不存在序列相关。
估计的模型为:D(Y) = 291.035 + 0.992*D(X)