计量经济学论文范文eviews

《我国财政收入影响因素分析》

班级：09财政1班 姓名：*** 学号：************ 指导教师：***

完成时间：2011年12月4日

摘要：对我国财政收入影响因素进行了定量分析，建立了数学模型，并提出了提高我国财政收入质量的建议。

关键词：财政收入 实证分析 影响因素

一、 引言

财政收入对于国民经济的运行及社会发展具有重要影响。首先，它是一个国家各项收入得以实现的物质保证。一个国家财政收入规模大小往往是衡量其经济实力的重要标志。其次，财政收入是国家对经济实行宏观的重要经济杠杆。宏观的首要问题是社会总需求与总供给的平衡问题，实现社会总需求与总供给的平衡，包括总量上的平衡和结构上的平衡两个层次的内容。财政收入的杠杆既可通过增收和减收来发挥总量作用，也可通过对不同财政资金缴纳者的财政负担大小的调整，来发挥结构调整的作用。此外，财政收入分配也是调整国民收入初次分配格局，实现社会财富公平合理分配的主要工具。在我国，财政收入的主体是税收收入。因此，在税收及不变的情况下，财政收入会随着经济繁荣而增加，随着经济衰退而下降。

我国的财政收入主要包括税收、国有经济收入、债务收入以及其他收入四种形式，因此，财政收入会受到不同因素的影响。从国民经济部门结构看，财政收入又表现为来自各经济部门的收入。财政收入的部门构成就是在财政收入中，由来自国民经济各部门的收入所占的不同比例来表现财政收入来源的结构，它体现国民经济各部门与财政

收入的关系。我国财政收入主要来自于工业、农业、商业、交通运输和服务业等部门。

因此，本文认为财政收入主要受到总税收收入、国内生产总值、其他收入和就业人口总数的影响。 二、 预设模型

令财政收入Y（亿元）为被解释变量，总税收收入X1（亿元）、国内生产总值X2（亿元）、其他收入X3（亿元）、就业人口总数为X4（万人）为解释变量，据此建立回归模型。 二、 数据收集

从《2010中国统计年鉴》得到1990--2009年每年的财政收入、总税收收入、国内生产总值工、其他收入和就业人口总数的统计数据如下：

obs 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

财政收入Y 总税收收入X1 国内生产总值X2 其他收入X3 就业人口总数X4

2937.1 3149.48 3483.37 4348.95 5218.1 6242.2 7407.99 8651.14 9875.95 11444.08 13395.23 16386.04 103. 21715.25 26396.47 319.29 38760.2 51321.78 61330.35

2821.86 2990.17 3296.91 4255.3 5126.88 6038.04 6909.82 8234.04 9262.8 10682.58 12581.51 15301.38 17636.45 20017.31 24165.68 28778. 34804.35 45621.97 223.79

18667.8 21781.5 26923.5 35333.9 48197.9 60793.7 71176.6 773 84402.3 677.1 99214.6 109655.2 120332.7 135822.8 159878.3 184937.4 216314.4 265810.3 314045.4

299.53 240.1 265.15 191.04 280.18 396.19 724.66 682.3 833.3 925.43 944.98 1218.1 1328.74 1691.93 2148.32 2707.83 3683.85 4457.96 5552.46

749 691 66152 66808 67455 68065 650 69820 70637 71394 72085 73025 73740 74432 75200 75825 700 76990 77480

2009 68518.3 59521.59 340506.9 7215.72 77995

三、 模型建立 1、 散点图分析

35000030000025000020000015000010000050000001000030000Y5000070000X1X2X3X4 2、 单因素或多变量间关系分析

Y X1 X2 X3 X4

Y 1 0.99134611

47853 90804 795 41508

1 18469 44782 93492

0.99347904520.9937402677

1 28471 80459

0.87701448860.85563773470.8561835802

1 50381

0.98360271980.98493529650.98624116560.8109403346

1

X1 47853

X2 90804 18469

X3 795 44782 28471

X4 41508 93492 80459 0.8109403346

50381

0.991346110.99347904520.87701448860.9836027198

0.99374026770.85563773470.9849352965

0.85618358020.9862411656

由散点图分析和变量间关系分析可以看出被解释变量财政收入Y与解释变量总税收收入X1、国内生产总值X2、其他收入X3、就业人口总数X4呈线性关系，因此该回归模型设为：

Y01X12X23X34X4

3、 模型预模拟

由eviews做ols回归得到结果：

Dependent Variable: Y Method: Least Squares Date: 11/14/11 Time: 17:51 Sample: 1990 2009 Included observations: 20

Variable C X1 X2 X3 X4

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

Coefficient 7299.523 1.062802 0.001770 0.873369 -0.115975

Std. Error 1691.814 0.021108 0.004528 0.119806 0.026580

t-Statistic 4.314614 50.34972 0.391007 7.2852 -4.363160

Prob. 0.0006 0.0000 0.7013 0.0000 0.0006 20556.75 19987.03 12.38886 12.63779 1667.9 0.000000

0.999978 Mean dependent var 0.999972 S.D. dependent var 106.62 Akaike info criterion 170537.9 Schwarz criterion -118.8886 F-statistic 1.496517 Prob(F-statistic)

Y7299.5231.062802X10.001770X20.873369X30.115975X4

(4.314614) ( 50.34972 ) ( 0.391007) ( 7.2852) ( -4.363160)

R20.999978 R0.999972 F1667.9 D.W1.496517

2四、 模型检验 1.计量经济学意义检验 ⑴多重共线性检验与解决

求相关系数矩阵，得到：

Correlation Matrix

Y 1 0.99134611

X1 47853 1

X2 90804

X3 795

X4 41508

0.991346110.99347904520.87701448860.9836027198

0.99374026770.85563773470.9849352965

47853

0.99347904520.9937402677

90804 795 41508

18469 44782 93492

18469 1 28471 80459

44782 28471 1 50381

93492 80459 0.8109403346

50381 1

0.85618358020.9862411656

0.87701448860.85563773470.8561835802

0.98360271980.98493529650.98624116560.8109403346

发现模型存在多重共线性。接下来运用逐步回归法对模型进行修正:

①将各个解释变量分别加入模型,进行一元回归: 作Y与X1的回归，结果如下:

Dependent Variable: Y Method: Least Squares Date: 11/22/11 Time: 23:02 Sample: 1990 2009 Included observations: 20

Variable C X1

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

Coefficient -755.6610 1.144994

Std. Error 145.2330 0.005760

t-Statistic -5.203094 198.7931

Prob. 0.0001 0.0000 20556.75 19987.03 15.09765 15.19722 39518.70 0.000000

0.9995 Mean dependent var 0.999519 S.D. dependent var 438.1521 Akaike info criterion 3455590. Schwarz criterion -148.9765 F-statistic 0.475046 Prob(F-statistic)

作Y与X2的回归，结果如下:

Dependent Variable: Y Method: Least Squares Date: 11/22/11 Time: 23:06 Sample: 1990 2009 Included observations: 20

Variable C X2

Coefficient -5222.077 0.2076

Std. Error 861.2067 0.0058

t-Statistic -6.063674 37.43267

Prob. 0.0000 0.0000

R-squared

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

0.987317 Mean dependent var 0.986612 S.D. dependent var 2312.610 Akaike info criterion 96267005 Schwarz criterion -182.2478 F-statistic 0.188013 Prob(F-statistic)

20556.75 19987.03 18.42478 18.52435 1401.205 0.000000

作Y与X3的回归，结果如下:

Dependent Variable: Y Method: Least Squares Date: 11/22/11 Time: 23:08 Sample: 1990 2009 Included observations: 20

Variable C X3

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

Coefficient 2607.879 10.03073

Std. Error 773.9988 0.294311

t-Statistic 3.369358 34.08209

Prob. 0.0034 0.0000 20556.75 19987.03 18.60971 18.70929 1161.5 0.000000

0.984740 Mean dependent var 0.9833 S.D. dependent var 2536.5 Akaike info criterion 1.16E+08 Schwarz criterion -184.0971 F-statistic 1.1943 Prob(F-statistic)

作Y与X4的回归，结果如下:

Dependent Variable: Y Method: Least Squares Date: 11/22/11 Time: 23:08 Sample: 1990 2009 Included observations: 20

Variable C X4

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

Coefficient -272959.3 4.097403

Std. Error 37203.65 0.518467

t-Statistic -7.33 7.902918

Prob. 0.0000 0.0000 20556.75 19987.03 21.29492 21.39449 62.45611 0.000000

0.776276 Mean dependent var 0.763846 S.D. dependent var 9712.824 Akaike info criterion 1.70E+09 Schwarz criterion -210.9492 F-statistic 0.157356 Prob(F-statistic)

②依据可决系数最大的原则选取X1作为进入回归模型的第一个解释变量,再依次将其余变量分别代入回归得:

作Y与X1、X2的回归，结果如下

Dependent Variable: Y Method: Least Squares Date: 11/22/11 Time: 23:09 Sample: 1990 2009 Included observations: 20

Variable C X1 X2

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

Coefficient -188.4285 1.281594 -0.025055

Std. Error 239.0743 0.049472 0.009029

t-Statistic -0.788159 25.90568 -2.774908

Prob. 0.4415 0.0000 0.0130 20556.75 19987.03 14.82405 14.97341 27118.20 0.000000

0.999687 Mean dependent var 0.999650 S.D. dependent var 374.0345 Akaike info criterion 2378330. Schwarz criterion -145.2405 F-statistic 0.683510 Prob(F-statistic)

作Y与X1、X3的回归，结果如下

Dependent Variable: Y Method: Least Squares Date: 11/22/11 Time: 23:10 Sample: 1990 2009 Included observations: 20

Variable C X1 X3

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

Coefficient -351.10 0.992813 1.356936

Std. Error 83.15053 0.018707 0.165109

t-Statistic -4.222527 53.07196 8.218410

Prob. 0.0006 0.0000 0.0000 20556.75 19987.03 13.59361 13.74297 92839.33 0.000000

0.999908 Mean dependent var 0.9998 S.D. dependent var 202.1735 Akaike info criterion 694859.9 Schwarz criterion -132.9361 F-statistic 1.177765 Prob(F-statistic)

作Y与X1、X4的回归，结果如下

Dependent Variable: Y Method: Least Squares Date: 11/22/11 Time: 23:10 Sample: 1990 2009 Included observations: 20

Variable C X1 X4

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

Coefficient 11853.46 1.185886 -0.1865

Std. Error 1824.522 0.0065 0.026984

t-Statistic 6.496748 178.4608 -6.917003

Prob. 0.0000 0.0000 0.0000 20556.75 19987.03 13.85886 14.00822 71206.90 0.000000

0.999881 Mean dependent var 0.999867 S.D. dependent var 230.84 Akaike info criterion 905931.0 Schwarz criterion -135.5886 F-statistic 1.459938 Prob(F-statistic)

③在满足经济意义和可决系数的条件下选取X3作为进入模型的第二个解释变量,再次进行回归则:

作Y与X1、X3、X2的回归，结果如下

Dependent Variable: Y Method: Least Squares Date: 11/22/11 Time: 23:13 Sample: 1990 2009 Included observations: 20

Variable C X1 X3 X2

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

Coefficient -76.04458 1.085924 1.210853 -0.014073

Std. Error 100.1724 0.029801 0.133444 0.003944

t-Statistic -0.759137 36.43881 9.073877 -3.567901

Prob. 0.4588 0.0000 0.0000 0.0026 20556.75 19987.03 13.10826 13.30741 104602.9

0.999949 Mean dependent var 0.999939 S.D. dependent var 155.5183 Akaike info criterion 386975.0 Schwarz criterion -127.0826 F-statistic

Durbin-Watson stat

1.196933 Prob(F-statistic)

0.000000

作Y与X1、X3、X4的回归，结果如下 Dependent Variable: Y Method: Least Squares Date: 11/22/11 Time: 23:13 Sample: 1990 2009 Included observations: 20

Variable C X1 X3 X4

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

Coefficient 6781.7 1.0682 0.1069 -0.107639

Std. Error 1024.745 0.014514 0.107949 0.0151

t-Statistic 6.618003 73.627 8.2551 -6.966675

Prob. 0.0000 0.0000 0.0000 0.0000 20556.75 19987.03 12.29900 12.49814 234970.9 0.000000

0.999977 Mean dependent var 0.999973 S.D. dependent var 103.76 Akaike info criterion 172276.1 Schwarz criterion -118.9900 F-statistic 1.451447 Prob(F-statistic)

④可见加入其余任何一个变量都会导致系数符号与经济意义不符,故最终修正后的回归模型为:

Dependent Variable: Y Method: Least Squares Date: 11/30/11 Time: 12:18 Sample: 1990 2009 Included observations: 20

Variable C X1 X3

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

Coefficient -351.10 0.992813 1.356936

Std. Error 83.15053 0.018707 0.165109

t-Statistic -4.222527 53.07196 8.218410

Prob. 0.0006 0.0000 0.0000 20556.75 19987.03 13.59361 13.74297 92839.33

0.999908 Mean dependent var 0.9998 S.D. dependent var 202.1735 Akaike info criterion 694859.9 Schwarz criterion -132.9361 F-statistic

Durbin-Watson stat

1.177765 Prob(F-statistic)

0.000000

Y351.100.992813X11.356936X3

(-4.222527) ( 53.07196) ( 8.218410)

R20.999908 R0.9998 F92839.33 D.W1.177765

2⑵异方差检验与修正

① 图示法

ee与X1的散点图如下：

200000160000120000EE800004000000100002000030000400005000060000X1 说明ee与X1存在单调递增型异方差性。

ee与X3的散点图如下：

200000160000120000EE80000400000020004000X360008000 说明ee与X3存在单调递增型异方差性。 ②G-Q检验

对20组数据剔除掉中间四组剩下的进行分组后， 第一组（1990-1997）数据的回归结果：

Dependent Variable: Y Method: Least Squares Date: 11/30/11 Time: 12: Sample: 1990 1997 Included observations: 8

Variable X1 X3 C

R-squared Adjusted R-squared

Coefficient 0.984123 0.851518 -28.34275

Std. Error 0.016255 0.156688 45.36993

t-Statistic 60.320 5.434472 -0.624703

Prob. 0.0000 0.0029 0.5596 5179.791 2099.840

0.999686 Mean dependent var 0.999560 S.D. dependent var

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

44.059 Akaike info criterion 9705.972 Schwarz criterion -39.75573 F-statistic 1.663630 Prob(F-statistic)

10.683 10.71872 7947.575 0.000000

残差平方和RSS1=9705.972 第二组（2002-2009）数据的回归结果：

Dependent Variable: Y Method: Least Squares Date: 11/30/11 Time: 12:55 Sample: 2002 2009 Included observations: 8

Variable X1 X3 C

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

Coefficient 1.0604 0.847228 -1184.159

Std. Error 0.027747 0.215114 261.8258

t-Statistic 38.43321 3.938503 -4.522698

Prob. 0.0000 0.0110 0.0063 39824.41 18639.16 13.52594 13.55573 36705.08 0.000000

0.999932 Mean dependent var 0.999905 S.D. dependent var 182.0047 Akaike info criterion 165628.5 Schwarz criterion -51.10375 F-statistic 1.326122 Prob(F-statistic)

残差平方和RSS2= 165628.5

所以F= RSS2/RSS1= 165628.5/9705.972=17.06 在给定=5%下查得临界值 F0.05(4,4)6.39，FF0.05(4,4)

因此否定两组子样方差相同的假设，从而该总体随机项存在递增异方差性。 ③White 方法检验

White Heteroskedasticity Test: F-statistic Obs*R-squared

6.142010 Probability 12.41812 Probability

0.003919 0.014498

Test Equation:

Dependent Variable: RESID^2 Method: Least Squares Date: 11/30/11 Time: 13:21 Sample: 1990 2009 Included observations: 20

Variable C X1 X1^2 X3 X3^2

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

Coefficient 24856.50 -20.57327 0.000212 237.1813 -0.024073

Std. Error 19211.30 7.9127 8.04E-05 78.61323 0.006568

t-Statistic 1.293848 -2.725252 2.639982 3.017067 -3.665230

Prob. 0.2153 0.0156 0.0186 0.0087 0.0023 34743.00 49156.00 23.92212 24.17105 6.142010 0.003919

0.620906 Mean dependent var 0.519815 S.D. dependent var 34062.86 Akaike info criterion 1.74E+10 Schwarz criterion -234.2212 F-statistic 1.560937 Prob(F-statistic)

nR2200.62090612.41812

=5%下，临界值20.05(4)9.488拒绝同方差性

④

修正

Coefficient -314.2074 0.979758 1.457291

Std. Error 43.68550 0.008622 0.065922

t-Statistic -7.192486 113.6336 22.10629

Prob. 0.0000 0.0000 0.0000 27246.27 74471.17 11.58127

Dependent Variable: Y Method: Least Squares Date: 11/30/11 Time: 14:29 Sample: 1990 2009 Included observations: 20 Weighting series: 1/E1

Variable C X1 X3

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

Weighted Statistics

0.999999 Mean dependent var 0.999999 S.D. dependent var 73.91795 Akaike info criterion

Sum squared resid 92885.67 Schwarz criterion 11.73063 Log likelihood -112.8127 F-statistic 3138195. Durbin-Watson stat

0.956075 Prob(F-statistic) 0.000000

Unweighted Statistics

R-squared 0.999902 Mean dependent var 20556.75 Adjusted R-squared 0.9991 S.D. dependent var 19987.03 S.E. of regression 209.0283 Sum squared resid 742778.2

Durbin-Watson stat

1.3683

Y314.20740.979758X11.457291X3

(-7.192486) ( 113.6336) ( 22.10629)

R20.999999 R20.999999 F3138195 D.W1.3683

⑶序列相关性检验

①从残差项e2与e2(-1)及e与时间t的关系图（如下）看，随机项呈现正序列相关性。

6004002001)-2(0E-200-400-600-600-400-2000200400600E2 6004002000-200-400-600909294969800E202040608 ②Q统计量检验

由图可以看出，存在一阶序列相关 ③回归检验

残差e2与e2（-1）做回归得：

Dependent Variable: E Method: Least Squares Date: 12/04/11 Time: 15:21 Sample (adjusted): 1991 2009

Included observations: 19 after adjustments

Variable C E(-1)

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

Coefficient 16.81525 0.303570

Std. Error 45.69611 0.231114

t-Statistic 0.367980 1.313508

Prob. 0.7174 0.2065 25.28519 201.1252 13.50553 13.60494 1.725303 0.20

0.092138 Mean dependent var 0.038734 S.D. dependent var 197.1916 Akaike info criterion 661036.6 Schwarz criterion -126.3025 F-statistic 1.7798 Prob(F-statistic)

e与e(-1)、e(-2)做回归得：

Dependent Variable: E Method: Least Squares Date: 12/04/11 Time: 15:24 Sample (adjusted): 1992 2009

Included observations: 18 after adjustments

Variable C E(-1) E(-2)

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

Coefficient 7.449760 0.4195 -0.3794

Std. Error 46.20912 0.244475 0.2781

t-Statistic 0.161218 1.716187 -1.363380

Prob. 0.8741 0.1067 0.1929 16.45940 203.1349 13.527 13.67629 1.788727 0.201043

0.192570 Mean dependent var 0.084912 S.D. dependent var 194.3193 Akaike info criterion 566399.7 Schwarz criterion -118.7510 F-statistic 2.055382 Prob(F-statistic)

由上表明不存在序列相关性。 ④D.W检验

由异方差检验修正后的结果：

Y314.20740.979758X11.457291X3

R20.999999 R0.999999 F3138195 D.W1.3683

2得D.W=1.3683

取=5%，由于n=20，k=3(包含常数项)，查表得： dl=1.10， du=1.

由于dlDependent Variable: E Method: Least Squares Date: 12/04/11 Time: 15:05 Sample (adjusted): 1992 2009

Included observations: 18 after adjustments

Variable

Y C E(-1) E(-2)

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

Coefficient 0.000984 -14.14792 0.392009 -0.347730

Std. Error 0.0028 73.42247 0.261633 0.298739

t-Statistic 0.386217 -0.192692 1.498316 -1.163992

Prob. 0.7051 0.8500 0.1563 0.2639 16.45940 203.1349 13.62841 13.82627 1.174565 0.3679

0.201082 Mean dependent var 0.029885 S.D. dependent var 200.0765 Akaike info criterion 560428.6 Schwarz criterion -118.6557 F-statistic 2.010385 Prob(F-statistic)

LMn*R2200.2010824.021

取=5%，2分布的临界值20.05(3)7.815 LM < 20.05(3)

故: 存在序列相关。 ⑥修正

为了更好的提高模型的精度，我们用广义差分法对模型进行修正。 首先用杜宾（durbin）两步法估计。

Dependent Variable: Y Method: Least Squares Date: 12/04/11 Time: 16:18 Sample (adjusted): 1992 2009

Included observations: 18 after adjustments

Variable C Y(-1) Y(-2) X1 X1(-1) X1(-2) X3 X3(-1) X3(-2)

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

Coefficient -36.85790 0.730610 0.358104 1.097355 -0.872470 -0.355699 0.755747 -0.272101 -0.083096

Std. Error 81.133 0.345304 0.3519 0.030377 0.400852 0.409249 0.218272 0.460341 0.402994

t-Statistic -0.453975 2.115847 0.982402 36.12488 -2.1761 -0.869149 3.462405 -0.591086 -0.206198

Prob. 0.6606 0.0635 0.3516 0.0000 0.0575 0.4073 0.0071 0.5690 0.8412 22502.69 20158.96 12.44630 12.149 78825.65 0.000000

0.999986 Mean dependent var 0.999973 S.D. dependent var 104.6672 Akaike info criterion 98597.03 Schwarz criterion -103.0167 F-statistic 2.219316 Prob(F-statistic)

由上表可得回归方程为广义差分结果。

Dependent Variable: Y1 Method: Least Squares

1=0.730610，2=0.358104，对原模型进行广义差分，下表

Std. Error 84.12065 0.0188 0.185136

t-Statistic -4.780018 .85006 5.981271

Prob. 0.0002 0.0000 0.0000 167.52

Date: 12/04/11 Time: 16:47 Sample (adjusted): 1992 2009

Included observations: 18 after adjustments

Variable C X11 X33

R-squared

Coefficient -402.0982 1.041509 1.107351

0.999844 Mean dependent var

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

0.999824 S.D. dependent var 196.6902 Akaike info criterion 580305.5 Schwarz criterion -118.9693 F-statistic 1.3856 Prob(F-statistic)

14812.28 13.55215 13.700 48198.10 0.000000

其中Y1=Y-0.730610*Y(-1)+0.358104*Y(-2),X11=X1-0.730610*X1(-1)+0.358104*X1(-2), X33=x3-0.730610*X3(-1)+0.358104*X3(-2)

D.W=1.3856下面我们用采用科克伦-奥科特迭代法估计

Dependent Variable: Y Method: Least Squares Date: 12/04/11 Time: 15:33 Sample (adjusted): 1991 2009

Included observations: 19 after adjustments Convergence achieved after 107 iterations

Variable Coefficient Std. Error t-Statistic Prob. C -21511.24 677371.7 -0.031757 0.9751 X1 1.086097 0.022027 49.306 0.0000 X3 0.825966 0.1230 6.406292 0.0000 AR(1)

0.995597

0.142149

7.0036 0.0000 R-squared 0.999968 Mean dependent var 21484.10 Adjusted R-squared 0.999962 S.D. dependent var 20087.80 S.E. of regression 123.6723 Akaike info criterion 12.65781 Sum squared resid 229422.7 Schwarz criterion 12.856 Log likelihood -116.2492 F-statistic 158291.4 Durbin-Watson stat 2.273071 Prob(F-statistic) 0.000000

Inverted AR Roots

1.00

取=5% ，du=1.表明:广义差分模型已不存在序列相关性。同时可决系数，统计量也均达到理想水平。 五、 模型的最终确定

t，FY-21511.241.086097X10.825966X3

(-0.031757) (49.306) (6.406292)

R20.999968 R0.999962 F158291.4 D.W2.273071

2