我国国债规模的计量经济学研究Word下载.docx
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1115
269.76
62.89
1982
82.86
5294.7
1124
1153.3
279.26
55.52
1983
79.41
5934.5
1249
1292.5
339.71
42.47
1984
77.34
7171
1501.9
1546.4
421
28.91
1985
89.85
8964.4
1866.4
1844.8
407.8
39.56
1986
138.25
10202.2
2260.3
2330.8
455.62
50.16
1987
223.55
11962.5
2368.9
2448.5
496.64
79.83
1988
270.78
4928.3
2628
2706.6
431.96
76.75
1989
407.97
16909.2
2947.9
3040.2
366.05
72.36
1990
375.45
18547.9
937.1
3083.59
393.03
190.07
1991
461.4
21617.8
3149.48
3386.62
380.43
246.8
1992
669.68
26638.1
3483.37
3742.2
347.46
438.57
1993
739.22
34634.4
4348.95
4642.3
483.67
336.22
1994
1175.25
46759.4
5218.1
5792.62
529.57
499.36
1995
1549.76
58478.1
6242.2
6823.72
621.05
882.96
1996
1967.28
67884.6
7407.99
7937.55
625.88
1355.03
1997
2476.82
74462.6
8651.14
9233.56
696.74
1918.37
1998
3310.93
78345.2
9875.95
10798.18
1197.39
2352.92
1999
3715.03
82067.5
11444.08
13187.67
1852.14
1910.53
2000
4180.1
89468.1
13395.23
15886.5
2109.45
1579.82
2001
4604
97314.8
16386.04
18902.58
2546.42
2007.73
2002
5679
105172.3
18903.64
22053.15
3160.96
2563.13
2003
6153.53
117251.9
21715.25
24649.95
2687.82
2952.24
各变量X与Y的散点图
通过上面的各散点图可看出,我国国债规模与财政收入、财政支出、GDP、预算内固定资产投资、还本付息支出都呈一定的线性关系。
模型：
四模型的估计与分析
对各模型的T、F检验值检验的列表
五元回归分析
Variable
Coefficient
t-statistic
Prob
F-statistic
R-squared
D.W
是否通过
C-X1-X2-X3-X4-X5
-345.5537
-8.992255
0.0000
1858.017
0.997959
1.810501
否
0.006730
2.262346
0.0356
-0.016139
-0.322531
0.7506
0.121801
2.363332
0.0289
0.515668
4.221127
0.0005
0.549246
6.232881
OLS五元回归结果：
DependentVariable:
Y
Method:
LeastSquares
Date:
12/15/07Time:
15:
12
Sample:
19792003
Includedobservations:
25
Std.Error
t-Statistic
Prob.
C
38.42793
0.002975
0.050038
0.051538
X4
0.122164
X5
0.088121
Meandependentvar
1543.154
AdjustedR-squared
0.997422
S.D.dependentvar
1931.091
S.E.ofregression
98.05162
Akaikeinfocriterion
12.21443
Sumsquaredresid
182668.3
Schwarzcriterion
12.50696
Loglikelihood
-146.6804
Durbin-Watsonstat
Prob（F-statistic）
0.000000
一元回归分析
C-X1
-448.5850
-3.152526
0.0045
362.1776
0.940287
0.292977
是
0.049623
19.03096
C-X2
-364.5689
-5.391607
1567.655
0.985541
1.259156
是
0.317144
39.59362
C-X3
-332.6220
-5.416529
1870.844
0.987855
0.647764
0.275712
43.25325
C-X4
-346.0281
-2.091096
0.0478
250.0647
0.915771
0.596218
2.152250
15.81343
C-X5
16.08748
0.118923
0.9064
309.5262
0.930833
0.640316
1.930964
17.59336
二元回归分析
C-X1-X2
-407.7123
-6.474013
976.5378
0.988861
1.267950
0.010617
2.560960
0.0178
0.253477
9.794750
C-X1-X3
-386.8159
-7.172490
1342.424
0.991872
0.869229
0.011202
3.297517
0.217592
11.81665
0.0033
C-X1-X4
-530.8702
-9.167249
1171.824
0.990700
1.260965
0.028894
13.31377
1.041602
10.92059
C-X1-X5
-280.4862
-1.827799
0.0812
212.1628
0.950709
0.313668
0.029163
2.978459
0.0069
0.825868
2.156691
0.0422
C-X2-X3
-343.7450
-5.464196
927.8328
0.988283
0.746555
0.089974
0.896392
0.3797
0.197795
2.269365
0.0334
C-X2-X4
-377.2239
-5.517876
790.4135
0.986274
1.087427
0.288965
10.63037
0.207530
1.084437
0.2899
C-X2-X5
-307.8153
-5.595258
1280.103
0.991480
1.448666
0.244649
12.51418
0.4796603
3.916285
0.0007
C-X3-X4
-326.7281
-5.105252
902.4216
0.987957
0.691342
0.286094
11.48361
-0.087182
-0.431625
0.6702
C-X3-X5
-282.4807
-7.532123
2639.611
0.995850
1.157476
0.207360
18.56535
0.524613
6.510090
C-X4-X5
-279.4430
-5.039876
1214.665
0.991025
1.531134
1.094873
12.14711
1.089423
13.58210
三元回归分析
C-X1-X2-X3
-387.6445
-6.984257
855.0460
0.991880
0.875519
0.011059
3.049745
0.0061
0.012276
0.137562
0.8919
0.207700
2.794013
0.0109
C-X1-X2-X4
-478.3601
-9.172912
1082.847
0.993577
1.185413
0.018649
4.886401
0.0001
0.120679
3.066923
0.0059
0.623230
3.926680
0.0008
C-X1-X2-X5
-322.1222
-4.944750
812.8847
0.991555
1.407913
0.002130
0.431177
0.6707
0.239094
10.07822
0.431898
2.588122
0.0172
C-X1-X3-X4
-450.0931
-8.091560
1088.976
0.993613
1.005350
0.017612
4.312228
0.0003
0.127501
3.094673
0.0055
0.477254
2.392226
0.0262
C-X1-X3-X5
-284.3679
-6.240434
1680.227
0.995851
1.152494
0.000268
0.076895
0.9394
0.206782
15.11612
0.518394
4.487648
0.0002
C-X1-X4-X5
-407.1286
-8.864284
1709.865
0.995923
1.830068
0.015189
5.022462
0.995655
15.26040
0.590109
5.186461
C-X2-X3-X4
-341.4010
-5.071613
590.8416
0.988291
0.753386
-0.085237
0.773796
0.4477
0.204995
1.901965
0.0710
-0.026009
-0.118955
C-X2-X3-X5
-271.2831
-6.940293
1761.802
0.996043
1.227619
-0.065103
-1.010700
0.3237
0.259446
4.920342
0.557565
6.416653
C-X2-X4-X5
-318.6732
-8.328615
1778.199
0.996079
1.596952
0.134950
5.202399
0.578291
4.962747
0.685929
7.246348
C-X3-X4-X5
-296.2823
-9.633111
2664.243
0.997379
1.533877
0.144178
7.135892
0.388169
3.500982
0.0021
0.654775
8.689441
四元回归分析
C-X1-X2-X3-X4
-459.1911
-7.981618
799.6049
0.993786
1.054507
0.017400
4.205210
0.0004
0.061463
0.745688
0.4645
0.070927
0.819540
0.4221
0.514582
2.476744
0.0223
C-X1-X2-X3-X5
-273.9660
-5.855657
1259.206
0.996045
1.220017
0.000386
0.110858
0.9128
-0.065354
-0.989874
0.3341
0.258813
4.765008
0.548716
4.589391
C-X1-X2-X4-X5
-384.4703
-9.987280
1888.219
0.997359
1.816283
0.009462
3.113560
0.087661
3.297898
0.0036
0.679501
6.629749
0.516264
5.351632
C-X1-X3-X4-X5
-349.2579
-9.744709
2431.420
0.997948
1.782340
0.006815
2.353420
0.107210
4.442419
0.524849
4.520295
0.542174
6.499667
C-X2-X3-X4-X5
-291.3452
-8.831018
1924.888
0.997409
1.578071
-0.026197
-0.478650
0.6374
0.167111
3.204587
0.0044
0.376041
3.248073
0.0040
0.663968
8.389671
通过检验的方程如下：
一元回归模型：
YX1:
Y=-3.152526+19.03096X1+e
YX2:
Y=-5.391607+39.59362X2+e
YX3:
Y=-5.416529+43.25325X3+e
YX4:
Y=-2.091096+15.81343X4+e
YX5:
Y=0.118923+17.59336X5+e
二元回归模型：
YX1X2:
Y=-6.474013+2.560960X1+9.794750X2+e
YX1X3:
Y=-7.172490+3.297517X1+11.81665X3+e
YX1X4:
Y=-9.167249+13.31377X1+10.92059X4+e
YX1X5:
Y=-1.827799+2.978459X1+2.156691X5+e
YX2X5;
Y=-5.595258+12.51418X2+3.916285X5+e
YX3X5:
Y=-7.532123+18.56535X3+6.510090X5+e
YX4X5:
Y=-5.039876+12.14711X4+13.58210X5+e
三元回归模型：
YX1X2X4:
Y=-9.172912+4.886401X1+3.066923X2+3.926680X4+e
YX1X3X4:
Y=-450.0931+0.017612X1+0.127501X3+0.477254X4+e
YX1X4X5:
Y=-407.1286+0.015189X1+0.995655X4+0.590109X5+e
YX3X4X5:
Y=-296.2823+0.144178X3+0.388169X4+0.654775X5+e
YX2X4X5:
Y=-318.6732+0.134950X2+0.578291X4+0.685929X5+e
四元回归模型：
YX1X2X4X5:
Y=-384.4703+0.009462X1+0.087661X2+0.679501X4+0.516264X5+e
YX1X3X4X5:
Y=-349.2579+0.006815X1+0.107210X3+0.524849X4+0.542174X5+e
经选取最优的方程:
：
（-9.633111）（7.135892）（3.500982）（8.689441）
R²
=0.997379DW=1.533877F=2664.243
从经济意义方面检验参数估计量，各值均大于零,没有明显的错误。
从统计检验来看，方程拟合优度很高，总体显著性很好；
至于变量的显著性，k=3、n
升级会员 