计量经济大作业(3)

⑵在方程窗口上点击View\\Residual Test\\White Heteroskedastcity,即可以得到检验结果。下图是怀特检验中no cross terms的结果。

White Heteroskedasticity Test: F-statistic

0.713421 Probability 4.917313 Probability

Std. Error

1.16E+08 58.13777 0.000177 242.9534 0.002606 2129086. 9756.465

t-Statistic

0.199172 -1.716907 1.130907 1.683536 -1.418741 -0.175701 0.175414

Coefficient

23149016 -99.81716 0.000201 409.0208 -0.003697 -374082.6 1711.416

0.645005 0.554461

Prob.

0.8450 0.1080 0.2771 0.1144 0.1779 0.8630 0.8633

Obs*R-squared

Test Equation:

Dependent Variable: RESID^2 Method: Least Squares Date: 12/15/12 Time: 05:19 Sample: 1991 2011 Included observations: 21

Variable C X1 X1^2 X2 X2^2 X3 X3^2

R-squared

0.234158 Mean dependent var -0.094060 S.D. dependent var 1651657. Akaike info criterion 3.82E+13 Schwarz criterion -326.2034 F-statistic 2.893273 Prob(F-statistic)

945767.9 1579063. 31.73366 32.08183 0.713421 0.645005

Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat

从上图中的怀特检验中我们可以发现P值很大，那么也就意味着该模型不存在异方差。

1.2 Goldfeld-Quant检验

A. 将样本按解释变量排序(SORT X1)并分成两部分(分别有1到10共10个样本合19到28共10个样本)

B. 利用样本1建立回归模型1，其残差平方和为RSS1=895534。 C. 利用样本2建立回归模型2，其残差平方和为RSS2=8271287。 D. 计算F统计量：F?RSS2/RSS1＝9.2362。

取??0.05时，查F分布表得F0.05(8?4,8?4)?6.39,F?9.24?F0.05?6.39，所以存在异方差性。 1.3 park检验

A. 建立回归模型。

B. 生成新变量序列：GENR LNE2=log(RESID^2)

GENR LNX1=log(X1) GENR LNX2=log(X2) GENR LNX3=log(X3)

C. 建立新残差序列对解释变量的回归模型：LS LNE2 C LNX1 LNX2 LNX3，回归结果如

????Dependent Variable: LNE2 Method: Least Squares Date: 12/21/12 Time: 19:18 Sample: 13 21

Included observations: 9

Variable C LNX1 LNX2 LNX3

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

Coefficient -140.7097 -11.31236 9.469729 41.31118

Std. Error 143.2992 28.26318 22.47073 44.37096

t-Statistic -0.981929 -0.400251 0.421425 0.931041

Prob. 0.3712 0.7055 0.6909 0.3946 12.22358 1.689082 4.370270 4.457925 4.181110 1.959886

0.249551 Mean dependent var -0.200719 S.D. dependent var 1.850850 Akaike info criterion 17.12823 Schwarz criterion -15.66621 Hannan-Quinn criter. 0.554226 Durbin-Watson stat 0.667410

D. 从上图所示的回归结果中可以看出，LNX1 LNX2 LNX3的系数估计值不为0但不能通过显著性检验，即随机误差项的方差与解释变量不是存在较强的相关关系，即认为不存在异方差性。

1.4Gleiser检验(Gleiser检验与Park检验原理相同)

A. 建立回归模型。

B. 生成新变量序列：GENR E=ABS(RESID)

C. 分别建立新残差序列(E)对各解释变量的回归模型：LS E C X1; LS E C X2;LS E C X3回归结果如下所示：

????Dependent Variable: E Method: Least Squares Date: 12/21/12 Time: 19:23 Sample: 13 21

Included observations: 9

Variable C X1

R-squared Adjusted R-squared S.E. of regression Sum squared resid

Coefficient 1.450207 -9.60E-07

Std. Error 0.727079 2.45E-06

t-Statistic 1.994566 -0.392606

Prob. 0.0863 0.7063 1.184311 0.750408 2.568480 2.612308

0.021546 Mean dependent var -0.118234 S.D. dependent var 0.793530 Akaike info criterion 4.407833 Schwarz criterion

Log likelihood F-statistic Prob(F-statistic)

????Dependent Variable: E Method: Least Squares

-9.558160 Hannan-Quinn criter. 0.154140 Durbin-Watson stat 0.706291

2.473900 2.739094

Coefficient 1.409742 -3.94E-06

Std. Error 0.605681 9.52E-06

t-Statistic 2.327530 -0.413617

Prob. 0.0528 0.6915

1.184311 0.750408 2.566115 2.609943 2.471535 2.750817

Date: 12/21/12 Time: 19:25 Sample: 13 21

Included observations: 9

Variable C X2

R-squared

0.023857 Mean dependent var -0.115592 S.D. dependent var 0.792593 Akaike info criterion 4.397421 Schwarz criterion -9.547518 Hannan-Quinn criter. 0.171079 Durbin-Watson stat 0.691533

Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

????Dependent Variable: E Method: Least Squares

Coefficient 15.64669 -0.141322

Std. Error 10.99407 0.107405

t-Statistic 1.423194 -1.315783

Prob. 0.1977 0.2297

1.184311 0.750408 2.369259 2.413086 2.274679 2.690820

Date: 12/21/12 Time: 19:24 Sample: 13 21

Included observations: 9

Variable C X3

R-squared

0.198285 Mean dependent var 0.083755 S.D. dependent var 0.718296 Akaike info criterion 3.611639 Schwarz criterion -8.661664 Hannan-Quinn criter. 1.731285 Durbin-Watson stat 0.229699

Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

由上述各回归结果可知，各回归模型中解释变量的 …… 此处隐藏：2999字，全部文档内容请下载后查看。喜欢就下载吧 ……