影响GDP增长的经济因素分析
组员：苏敏（分析、撰文）；孙戎（协助撰文、编辑排版、搜集资料）；曾炯、李黎、蒋文（搜集资料） 近年来，我国GDP逐年增长，经济发展速度令人瞩目。为更好的了解我国经济增长的原因，我组对影响我国GDP增长的经济因素进行了分析。
下表（表1.1）提供了我国1978——2002年的GDP及其主要影响因素的数据。其中Y=GDP(亿元)；X1＝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＝农业总产值（亿元）；X5＝社会消费品零售总额（亿元）；X6＝进出口贸易总额（亿元）
Obs 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
X1 57144 58588 60275 59447 62067 66040 70904 76682 80850 86632 92997 96934 98703 103783 109170 115993 122737 131176 138948 138173 132214 130119 130297 134914 148222
X2 40152 41024 42361 43725 45295 46436 48197 49873 51282 52783 54334 55329 56740 58360 59432 60220 61470 62388 68850 69600 70637 71394 72085 73025 73740
X3 184 197 236 249 266 289 327 437 447 508 635 762 803 896 1070 1331 1746 2236 2641 2834 2972 3138 3397 3609 3791
X4 1397 1697.6 1922.6 2180.62 2483.26 2750 3214.13 3619.49 4013.01 4675.7 5865.27 6534.73 7662.09 8157.03 9084.7 10995.5 15750.5 20340.9 22353.7 23788.4 24541.9 24519.1 32917.93 37213.49 43499.91
X5 1558.6 1800 2140 2350 2570 2849.4 3376.4 4305 4950 5820 7440 8101.4 8300.1 9415.6 10993.7 12462.1 16264.7 20620 24774.1 27298.9 29152.5 31134.7 34152.6 37595.2 40910.5
X6 355 454.6 570 735.3 771.3 860.1 1201 2066.7 2850.4 3084.2 3822 4155.9 5560.1 7229.3 9119.6 11271 20381.9 23499.9 24133.8 26967.2 26849.7 29896.2 39273.2 42183.6 51378.2
Y 3624.1 4038.2 4517.8 4862.4 5294.7 5934.5 7171 8964.4 10202.2 11962.5 14928.3 16909.2 18547.9 21617.8 26638.1 34634.4 46759.4 58478.1 67884.6 74462.6 78345.2 82067.5 89468.1 97314.8 104790.6
一：现估计模型为Y=A0+A1*X1+A2*X2+A3*X3+A4*X4+A5*X5+A6*X6+U 运用OLS估计方法对上式中得参数进行估计，利用Eviews软件得回归分析结果如下： （表1.2）
Dependent Variable: Y Method: Least Squares Date: 06/03/05 Time: 20:44 Sample: 1978 2002 Included observations: 25 Variable C
Coefficient 5421.215
Std. Error 3244.856
t-Statistic 1.670710
Prob. 0.1121
1
X1 X2 X3 X4 X5 X6 R-squared
Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat
0.050933 -0.224428 21.18387 -0.216535 0.488490 0.336620 0.030215 0.113375 2.084941 0.203877 0.311803 0.139208 1.685708 -1.979516 10.16042 -1.062084 1.566665 2.418102 0.1091 0.0633 0.0000 0.3022 0.1346 0.0264 34444.88 16.05080 16.39208 10925.17 0.000000
0.999725 Mean dependent var 35976.74 0.999634 S.D. dependent var 658.9930 Akaike info criterion 7816893. Schwarz criterion -193.6350 F-statistic 1.748019 Prob(F-statistic)
分析回归结果： 从经济意义上讲，就业人口X2的系数为负，可初步认为国民经济在向技术密集型、资本密集型发展。农业总产值的系数为负，不符合实际经济意义。其余解释变量的系数为正，符合实际经济现象。
从模型检验上讲，拟合较好。可决系数R^（2）=0.999725,F统计量为10925.17>2.66=F0.05(6,18)表明模型在整体上拟合非常好；系数显著性检验：对于A0,t统计量为1.670710，给定ａ＝0.05 查t分布表，在自由度为n-k=18下，得临界值T0.025(18)=2.101
因为t 现重新估计模型为Y=A1X1+A2X2+A3X3+A4X4+A5X5+A6X6 得回归结果如下（表1.3）： Dependent Variable: Y Method: Least Squares Date: 06/03/05 Time: 20:57 Sample: 1978 2002 Included observations: 25 Variable X1 X2 X3 X4 X5 X6 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Coefficient 0.010656 -0.041248 22.68974 -0.140006 0.146023 0.387108 Std. Error 0.019053 0.030184 1.966670 0.207819 0.245779 0.142150 t-Statistic 0.559269 -1.366564 11.53714 -0.673692 0.594124 2.723241 Prob. 0.5825 0.1877 0.0000 0.5086 0.5594 0.0135 0.999683 Mean dependent var 35976.74 0.999599 S.D. dependent var 689.3576 Akaike info criterion 9029064. Schwarz criterion -195.4370 Durbin-Watson stat 34444.88 16.11496 16.40749 1.704338 从模型检验上看，R^(2)=0.999683小于第一次模型的可决系数;T检验也并不优于第一次模型的t检验，故仍采用第一次模型。 二、多重共线性检验 1、检验： 2 利用Eviews计算线性回归模型中，六个解释变量的如下简单相关系数矩阵(表2.1.1)： X1 X1 1 X2 0.978454327431 X3 0.92085407884 X4 0.888167174119 X5 0.911118877104 X6 0.883472715462 X2 0.978454327431 1 0.948815761328 0.917125867946 0.946036086594 0.909232951166 X3 0.92085407884 0.948815761328 X4 0.888167174119 0.917125867946 X5 0.911118877104 0.946036086594 X6 0.883472715462 0.909232951166 1 0.982786453939 0.997008287295 0.982361794167 0.982786453939 1 0.990709311732 0.997396156937 0.997008287295 0.990709311732 1 0.98844222336 0.982361794167 0.997396156937 0.98844222336 1 从上表可以看出，各解释变量之间存在高度线性相关。同时由表1.2又可看出，尽管整体上线性回归拟合较好，但X1 X2 X4 X5 变量的参数T值并不显著，表明模型中解释变量确实存在严重的多重共线性。 2、修正： ⑴运用OLS方法逐一求出Y对各个解释变量的回归，分别如下： Y=-67070.34+1.029232X1 （式2.1.1） (9781.140) (0.093575) t=(-6.856618) (10.99902) R^(2)=0.840254 Se=14063.12 F=120.9784 Y=-133299.7+2.962005X2 （式2.1.2） (12588.50) (0.212476) t=(-10.58901) (13.68286) R^(2)=0.890591 Se=11638.38 F=187.2206 Y=-2268.943+27.31756X3 （式2.1.3） (348.7497) (0.186822) t=(-6.505936) (146.2225) R^(2)=0.998925 Se=1153.406 F=21381.06 Y=617.7713+2.752282X4 （式2.1.4） (1669.79)(0.094620) t=(0.369969) (29.08787) R^(2)=0.973536 Se=5723.931 F=846.1039 Y=-1873.193+2.700977X5 （式2.1.5） (712.2024) (0.037971) t=(-2.630142) (71.13173) R^(2)=0.995475 Se=2366.912 F=5059.723 Y=5875.266+2.222034X6 （式2.1.6） (1531.230) (0.075790) t=（3.836958）（29.31845） R^(2)=0.973940 Se=5680.092 F=859.5713 综合分析可见，在七个一元回归模型中，GDP(Y)对居民消费水平(X3)线性关系强，拟合程度好。 3 （2）将其余解释变量逐一带入对X3的一元线性回归方程中，得以下几个模型： Y=-387.7386-0.027368X1+27.93105X3 （式2.2.1） (1367.242) (0.019261) (0.468871) t=(-0.283592) (-1.420886) (59.57089) ￣R^(2)=0.998926 Se=1128.676 F=11165.14 Y=6262.980+0.029858X1-0.232206X2+28.56686X3 （式2.2.2） (3643.674) (0.034485) (0.119009) (0.548771) t=(1.718864) (0.865831) (-1.951160) (52.05602) ￣R^(2)=0.999048 Se=1062.902 F=8394.415 Y=4027.197+0.032089X1-0.181952X2+24.94421X3+0.327880X4 （式2.2.3） (2612.367) (0.024319) (0.084582) (0.860040) (0.069519) t=(1.541590) (1.319521) (-2.151190) (29.00354) (4.716419) ￣R^(2)=0.999606 Se=749.4065 F=12670.52 Y=7124.543+0.061735X1-0.295985X2+22.25771X3+0.173648X4+0.442008X5 （式2.2.4） (3548.587)(0.033478) (0.122613) (2.282200) (0.13910) (0.348654) t=(2.007713)(1.844017) (-2.413968) (9.753743) (1.243811) (1.267755) ￣R^(2)=0.999541 Se=738.2831 F=10444.48 Y=5421.215+0.050933X1-0.224428X2+21.18387X3-0.216535X4+0.488490X5+0.336620X6 (式2.2.5) （3244.856）（0.030215）（0.113375）（2.084941）（0.203877）（0.311803）（0.139208） t=（1.670710）（1.685708）（-1.979516）（10.16042）（-1.062084）（1.566665）（2.418102） ￣R^(2)=0.999634 Se=658.9930 F=10925.17 从式（2.2.1）可以看出，解释变量X1与X2之间存在共线性。又因为X2对Y的经济意义影响低于X1，故舍去X2。 从式（2.2.4）（2.2.5）看出，解释变量X4 X5对Y的影响并不显著，故将X4 X5撤去得如下模型（表2.2.1）： Dependent Variable: Y Method: Least Squares Date: 06/03/05 Time: 22:06 Sample: 1978 2002 Included observations: 25 Variable C X1 X3 X6 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat Coefficient -903.4074 -0.005261 23.63361 0.318814 Std. Error 829.1531 0.012145 0.740702 0.050783 t-Statistic -1.089554 -0.433206 31.90706 6.277944 Prob. 0.2883 0.6693 0.0000 0.0000 0.999658 Mean dependent var 35976.74 0.999609 S.D. dependent var 681.1097 Akaike info criterion 9742120. Schwarz criterion -196.3871 F-statistic 1.631881 Prob(F-statistic) 34444.88 16.03097 16.22599 20452.98 0.000000 表2.2.1中能源消费总量x1的系数为负,不符合实际经济意义,现舍去1978至1982年的数据,重新回归如下: (表2.2.2) 4 Dependent Variable: Y Method: Least Squares Date: 06/03/05 Time: 22:10 Sample: 1983 2002 Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. C -1797.334 1438.251 -1.249666 0.2294 X1 0.004777 0.018343 0.260447 0.7978 X3 23.50009 0.841880 27.91382 0.0000 X6 0.317367 0.056410 5.626057 0.0000 R-squared 0.999589 Mean dependent var 43854.06 Adjusted R-squared 0.999512 S.D. dependent var 34234.35 S.E. of regression 756.2503 Akaike info criterion 16.27148 Sum squared resid 9150632. Schwarz criterion 16.47062 Log likelihood -158.7148 F-statistic 12973.20 Durbin-Watson stat 1.689291 Prob(F-statistic) 0.000000 经过上述逐步回归分析，表明y对x1 x3 x6的回归模型最优。 三、异方差性检验 1、检验 （1）、Goldfeld—Quandt检验 用OLS方法求得下列结果： Y=-12754.36+0.236669X1+10.96853X3-0.395909X6 （1983——1989） (6250.887) (0.1134474) (3.689418) (0.767691) ￣R^(2)=0.994022 ∑e1^(2)=287719.1 Y=-5129.215+0.035047X1+23.39430X3+0.304576X6 (1994——2002) （7786.425）（0.065303）（1.268709）(0.079944) ￣R^(2)=0.996966 ∑e2^(2)=5139285 求F统计量： F=∑e2^(2)/∑e1^(2)=17.86216139 给定显著性水平ａ=0.05，得临界值F0.05(4,4)=4.28,比较F=17.86216139＞ F0.05(4,4)=6.39可初步认为不存在异方差。 （2）、Arch检验 利用eviews软件输出结果为（表3.1.1） Dependent Variable: E2 Method: Least Squares Date: 06/03/05 Time: 23:01 Sample(adjusted): 1986 2002 Included observations: 17 after adjusting endpoints Variable Coefficient Std. Error t-Statistic Prob. C 602213.2 337049.7 1.786719 0.0973 E2(-1) -0.299677 0.304742 -0.983380 0.3434 E2(-2) 0.087203 0.289528 0.301188 0.7680 5 E2(-3) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat -0.005035 0.542470 -0.009282 0.9927 699258.1 30.04040 30.23645 0.567116 0.646348 0.115727 Mean dependent var 495758.6 -0.088335 S.D. dependent var 729489.3 Akaike info criterion 6.92E+12 Schwarz criterion -251.3434 F-statistic 1.948353 Prob(F-statistic) 丛输出的辅助回归函数重的R^(2)计算（n-p）*R^(2)=17*0.115727=1.967359,查χ^(2)分布表 给定α=0.05得临界值χ^(2)0.05（3）=7.81因为（n-p）*R^(2)=17*0.115727=1.967359＜χ^(2)0.05（3）=7.81所以拒绝H0,表明不存在异方差。 四、自相关检验 1、检验 （1）、图示法 由表2.2.2的OLS估计可直接得到残差resid,生成序列E,输出结果如下图： 30000002000000E2(-1)1000000001000000E220000003000000 由此图可以看出，残差et不成线性自回归，可初步认为随机误差ut不存在自相关。 （2）DW检验 根据表2.2.2估计的结果，DW=1.689291.给定显著性水平α=0.05，查DW表n=20,k=3得下限临界值dl=0.998 上限临界值du=1.676 因为du 1，对Y ,X1估计如下有限分布滞后模型 Y=A0+B0X1+B1X1（-1）+B2X1(-2)+B3X1(-3) 应用EVIEWS软件得回归分析结果如下： Dependent Variable: Y Method: Least Squares Date: 06/04/05 Time: 23:27 Sample: 1983 2002 Included observations: 20 Variable C Coefficient Std. Error -78223.17 10398.98 t-Statistic -7.522195 Prob. 0.0000 6 PDL01 -0.914296 0.400006 -2.285705 0.0362 PDL02 -0.828224 0.410601 -2.017099 0.0608 PDL03 1.087286 0.388824 2.796349 0.0129 R-squared 0.935158 Mean dependent var 43854.06 Adjusted R-squared 0.923001 S.D. dependent var 34234.35 S.E. of regression 9499.609 Akaike info criterion 21.33275 Sum squared resid 1.44E+09 Schwarz criterion 21.53189 Log likelihood -209.3275 F-statistic 76.91841 Durbin-Watson stat 0.481394 Prob(F-statistic) 0.000000 Lag i CoefficienStd. Error T-Statistic Distribution of X1 t . * | 0 1.00121 0.44552 2.24728 * . | 1 -0.91430 0.40001 -2.28570 * . | 2 -0.65523 0.39089 -1.67625 . *| 3 1.77840 0.45803 3.88274 Sum of 1.21009 0.08629 14.0238 Lags 即是Y＝-78223.17+1.00121X1－0.91430X1(-1) -0.65523X1(-2)+ 1.77840X1(-3) 2对Y ,X3有限分布滞后模型 Y=A0+B0X3+B1X3(-1)+B2X3(-2)+B3X3(-3) 用EVIEWS软件显示回归分如下： Dependent Variable: Y Method: Least Squares Date: 06/04/05 Time: 23:31 Sample: 1983 2002 Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. C -2497.770 516.2504 -4.838292 0.0002 PDL01 2.106917 2.176725 0.967930 0.3475 PDL02 -15.95829 2.156581 -7.399811 0.0000 PDL03 8.550866 2.150222 3.976737 0.0011 R-squared 0.998874 Mean dependent var 43854.06 Adjusted R-squared 0.998663 S.D. dependent var 34234.35 S.E. of regression 1251.806 Akaike info criterion 17.27942 Sum squared resid 25072277 Schwarz criterion 17.47856 Log likelihood -168.7942 F-statistic 4731.442 Durbin-Watson stat 1.165284 Prob(F-statistic) 0.000000 Lag i CoefficienStd. Error T-Statistic Distribution of X3 t . *| 0 26.6161 2.22221 11.9773 .* | 1 2.10692 2.17672 0.96793 * . | 2 -5.30051 2.11824 -2.50232 . * | 3 4.39380 2.45928 1.78662 7 Sum of 27.8163 Lags 0.33326 83.4682 即是Y＝-2497.770+26.6161 X3+2.10692 X3 (-1) -5.30051 X3 (-2)+ 4.39380 X3 (-3) 3对Y , x6有限分布滞后模型 Y=A0+B0 x6+B1 x6 (-1)+B2 x6 (-2)+B3 x6 (-3) 用EVIEWS软件显示回归分如下： Dependent Variable: Y Method: Least Squares Date: 06/04/05 Time: 23:37 Sample: 1983 2002 Included observations: 20 Variable C PDL01 PDL02 PDL03 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat Lag Distribution of X6 . *| . *| . * | . * | Lags Coefficient Std. Error 8207.606 1631.510 0.774028 0.343247 -0.075852 0.363485 -0.064975 0.330846 t-Statistic 5.030680 2.255015 -0.208680 -0.196391 Prob. 0.0001 0.0385 0.8373 0.8468 43854.06 34234.35 19.96772 20.16687 316.7499 0.000000 0.983441 Mean dependent var 0.980336 S.D. dependent var 4800.586 Akaike info criterion 3.69E+08 Schwarz criterion -195.6772 F-statistic 0.342226 Prob(F-statistic) i Coefficient 0 0.78491 1 0.77403 2 0.63320 3 0.36242 0.39271 0.34325 0.34422 0.46095 0.14135 Std. Error T-Statistic 1.99867 2.25501 1.83951 0.78625 18.0730 Sum of 2.55456 即是Y=8207.606+0.78491 x6+0.77403 x6 (-1)+ 0.63320 x6 (-2)+ 0.36242 x6 (-3) 通过以上一系列统计检验可以说明：我国GDP的增长与能源消费总量X1,居民消费水平X3，进出口贸易总额X6有很高的相关性。其中，又以居民消费水平X3的影响程度最为显著。由此可以看出影响我国GDP的主要因素是居民消费水平，进出口贸易总额，能源消费总量。 附本（相关表格） （表2.1.2） Dependent Variable: Y Method: Least Squares Date: 06/03/05 Time: 21:08 Sample: 1978 2002 Included observations: 25 8 Variable Coefficient Std. Error t-Statistic Prob. C -67070.34 9781.840 -6.856618 0.0000 X1 1.029232 0.093575 10.99902 0.0000 R-squared 0.840254 Mean dependent var 35976.74 Adjusted R-squared 0.833308 S.D. dependent var 34444.88 S.E. of regression 14063.12 Akaike info criterion 22.01712 Sum squared resid 4.55E+09 Schwarz criterion 22.11463 Log likelihood -273.2140 F-statistic 120.9784 Durbin-Watson stat 0.094594 Prob(F-statistic) 0.000000 (表2.1.3) Dependent Variable: Y Method: Least Squares Date: 06/03/05 Time: 21:12 Sample: 1978 2002 Included observations: 25 Variable Coefficient Std. Error t-Statistic Prob. C -133299.7 12588.50 -10.58901 0.0000 X2 2.962005 0.216476 13.68286 0.0000 R-squared 0.890591 Mean dependent var 35976.74 Adjusted R-squared 0.885834 S.D. dependent var 34444.88 S.E. of regression 11638.38 Akaike info criterion 21.63862 Sum squared resid 3.12E+09 Schwarz criterion 21.73613 Log likelihood -268.4828 F-statistic 187.2206 Durbin-Watson stat 0.159690 Prob(F-statistic) 0.000000 (表2。1.4) Dependent Variable: Y Method: Least Squares Date: 06/03/05 Time: 21:15 Sample: 1978 2002 Included observations: 25 Variable Coefficient Std. Error t-Statistic Prob. C -2268.943 348.7497 -6.505936 0.0000 X3 27.31756 0.186822 146.2226 0.0000 R-squared 0.998925 Mean dependent var 35976.74 Adjusted R-squared 0.998879 S.D. dependent var 34444.88 S.E. of regression 1153.406 Akaike info criterion 17.01544 Sum squared resid 30597948 Schwarz criterion 17.11295 Log likelihood -210.6931 F-statistic 21381.06 Durbin-Watson stat 0.841840 Prob(F-statistic) 0.000000 (表2.1.5) Dependent Variable: Y Method: Least Squares Date: 06/03/05 Time: 21:17 Sample: 1978 2002 9 Included observations: 25 Variable Coefficient Std. Error t-Statistic Prob. C 617.7713 1669.790 0.369969 0.7148 X4 2.752282 0.094620 29.08787 0.0000 R-squared 0.973536 Mean dependent var 35976.74 Adjusted R-squared 0.972385 S.D. dependent var 34444.88 S.E. of regression 5723.931 Akaike info criterion 20.21932 Sum squared resid 7.54E+08 Schwarz criterion 20.31683 Log likelihood -250.7415 F-statistic 846.1039 Durbin-Watson stat 0.557749 Prob(F-statistic) 0.000000 ( 表2.1.6) Dependent Variable: Y Method: Least Squares Date: 06/03/05 Time: 21:21 Sample: 1978 2002 Included observations: 25 Variable Coefficient Std. Error t-Statistic Prob. C -1873.193 712.2024 -2.630142 0.0150 X5 2.700977 0.037971 71.13173 0.0000 R-squared 0.995475 Mean dependent var 35976.74 Adjusted R-squared 0.995278 S.D. dependent var 34444.88 S.E. of regression 2366.912 Akaike info criterion 18.45318 Sum squared resid 1.29E+08 Schwarz criterion 18.55069 Log likelihood -228.6647 F-statistic 5059.723 Durbin-Watson stat 0.288053 Prob(F-statistic) 0.000000 (表2.1.7) Dependent Variable: Y Method: Least Squares Date: 06/03/05 Time: 21:23 Sample: 1978 2002 Included observations: 25 Variable Coefficient Std. Error t-Statistic Prob. C 5875.266 1531.230 3.836958 0.0008 X6 2.222034 0.075790 29.31845 0.0000 R-squared 0.973940 Mean dependent var 35976.74 Adjusted R-squared 0.972807 S.D. dependent var 34444.88 S.E. of regression 5680.092 Akaike info criterion 20.20394 Sum squared resid 7.42E+08 Schwarz criterion 20.30145 Log likelihood -250.5493 F-statistic 859.5713 Durbin-Watson stat 0.739809 Prob(F-statistic) 0.000000 （表2.2.1） Dependent Variable: Y Method: Least Squares 10 Date: 06/03/05 Time: 21:28 Sample: 1978 2002 Included observations: 25 Variable Coefficient Std. Error t-Statistic Prob. C -387.7386 1367.242 -0.283592 0.7794 X1 -0.027368 0.019261 -1.420886 0.1694 X3 27.93105 0.468871 59.57089 0.0000 R-squared 0.999016 Mean dependent var 35976.74 Adjusted R-squared 0.998926 S.D. dependent var 34444.88 S.E. of regression 1128.676 Akaike info criterion 17.00765 Sum squared resid 28026027 Schwarz criterion 17.15391 Log likelihood -209.5956 F-statistic 11165.14 Durbin-Watson stat 0.973989 Prob(F-statistic) 0.000000 (表2.2.2) Dependent Variable: Y Method: Least Squares Date: 06/03/05 Time: 21:31 Sample: 1978 2002 Included observations: 25 Variable Coefficient Std. Error t-Statistic Prob. C 6262.980 3643.674 1.718864 0.1003 X1 0.029858 0.034485 0.865831 0.3964 X2 -0.232206 0.119009 -1.951160 0.0645 X3 28.56686 0.548771 52.05602 0.0000 R-squared 0.999167 Mean dependent var 35976.74 Adjusted R-squared 0.999048 S.D. dependent var 34444.88 S.E. of regression 1062.902 Akaike info criterion 16.92104 Sum squared resid 23724995 Schwarz criterion 17.11606 Log likelihood -207.5130 F-statistic 8394.415 Durbin-Watson stat 0.984561 Prob(F-statistic) 0.000000 (表2.2.3) Dependent Variable: Y Method: Least Squares Date: 06/03/05 Time: 21:33 Sample: 1978 2002 Included observations: 25 Variable Coefficient Std. Error t-Statistic Prob. C 4027.197 2612.367 1.541590 0.1388 X1 0.032089 0.024319 1.319521 0.2019 X2 -0.181952 0.084582 -2.151190 0.0439 X3 24.94421 0.860040 29.00354 0.0000 X4 0.327880 0.069519 4.716419 0.0001 R-squared 0.999606 Mean dependent var 35976.74 Adjusted R-squared 0.999527 S.D. dependent var 34444.88 11 S.E. of regression 749.4065 Akaike info criterion 16.25330 Sum squared resid 11232201 Schwarz criterion 16.49707 Log likelihood -198.1662 F-statistic 12670.52 Durbin-Watson stat 1.562943 Prob(F-statistic) 0.000000 (表2.2.4) Dependent Variable: Y Method: Least Squares Date: 06/03/05 Time: 21:40 Sample: 1978 2002 Included observations: 25 Variable Coefficient Std. Error t-Statistic Prob. C 7124.543 3548.587 2.007713 0.0591 X1 0.061735 0.033478 1.844017 0.0808 X2 -0.295985 0.122613 -2.413968 0.0260 X3 22.25771 2.282200 9.752743 0.0000 X4 0.173648 0.139610 1.243811 0.2287 X5 0.442008 0.348654 1.267755 0.2202 R-squared 0.999636 Mean dependent var 35976.74 Adjusted R-squared 0.999541 S.D. dependent var 34444.88 S.E. of regression 738.2831 Akaike info criterion 16.25209 Sum squared resid 10356175 Schwarz criterion 16.54463 Log likelihood -197.1512 F-statistic 10444.48 Durbin-Watson stat 1.408484 Prob(F-statistic) 0.000000 (表3.1.1) Dependent Variable: Y Method: Least Squares Date: 06/03/05 Time: 22:21 Sample: 1983 1989 Included observations: 7 Variable Coefficient Std. Error t-Statistic Prob. C -12754.36 6250.887 -2.040409 0.1340 X1 0.236669 0.113474 2.085666 0.1283 X3 10.96853 3.689418 2.972970 0.0589 X6 -0.395909 0.767691 -0.515715 0.6417 R-squared 0.997011 Mean dependent var 10867.44 Adjusted R-squared 0.994022 S.D. dependent var 4005.294 S.E. of regression 309.6875 Akaike info criterion 14.60456 Sum squared resid 287719.1 Schwarz criterion 14.57366 Log likelihood -47.11597 F-statistic 333.5425 Durbin-Watson stat 2.231426 Prob(F-statistic) 0.000277 (表3.1.2) Dependent Variable: Y Method: Least Squares 12 Date: 06/03/05 Time: 22:31 Sample: 1994 2002 Included observations: 9 Variable Coefficient Std. Error t-Statistic Prob. C -5129.215 7786.425 -0.658738 0.5392 X1 0.035047 0.065303 0.536687 0.6145 X3 23.39430 1.268709 18.43945 0.0000 X6 0.304576 0.079944 3.809862 0.0125 R-squared 0.998104 Mean dependent var 77730.10 Adjusted R-squared 0.996966 S.D. dependent var 18405.97 S.E. of regression 1013.833 Akaike info criterion 16.98197 Sum squared resid 5139285. Schwarz criterion 17.06962 Log likelihood -72.41885 F-statistic 877.2615 Durbin-Watson stat 2.005411 Prob(F-statistic) 0.000000 （表4.2.1） Dependent Variable: DY Method: Least Squares Date: 05/07/05 Time: 16:55 Sample(adjusted): 1979 2002 Included observations: 24 after adjusting endpoints Variable Coeffic Std. t-Statisti Prob. ient Error c DX2 -0.0320.00834 -3.9276 0.000 787 8 57 8 DX3 25.195 1.01434 24.8396 0.000 94 3 6 0 DX6 0.1775 0.08910 1.99303 0.059 88 4 3 4 R-squared 0.9988 Mean dependent -447173 var 0.72 Adjusted 0.9987 S.D. dependent 4496R-squared 65 var 8.62 S.E. of 1580.1 Akaike info 17.68regression 62 criterion 491 Sum squared 52435 Schwarz 17.83resid 167 criterion 217 Log likelihood -209.2 Durbin-Watson 1.926189 stat 203 13 因篇幅问题不能全部显示，请点此查看更多更全内容