金融计量学例题Word下载.docx

金融计量学例题Word下载.docx

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10414

3996

12693.94

791

107.4

6.5

1993

447.67

131156

13134

4689

16681.34

607

114.4

6

1994

404.02

6127

15033

6876

22131.88

714

110.8

4.75

1995

409.51

27419

17389

8636

31353.64

911

99.4

1996

619.17

25633

21715

12339

43528.81

1231

91.1

9.5

1997

1121.17

95684

27075

16623

70752.98

2760

90.8

10

1998

1506.84

105987

31827

19937

125989.84

2651

86.3

16

1999

1105.79

46230

35393

24787

99468.48

2105

125.3

10.5

2000

933.03

37165

38832

25112

82478.3

3030

2001

1008.54

48787

46079

24414

54936.3

2810

106.6

8.5

2002

1567.56

75808

47871

22970

87135.51

2649

115.7

2003

1960.06

123128

54372

24403

129884.03

3031

110.1

2004

2884.88

371406

65602

30531

163044.2

3644

105.8

5

2005

2556.72

198569

74917

37861

215033.62

3690

101.6

5.25

三、多重共线性分析

可用逐步回归法进行变量选择

DependentVariable:

Y

Method:

LeastSquares

Date:

12/20/09Time:

22:

07

Sample:

19741988

Includedobservations:

15

Variable

Coefficient

Std.Error

t-Statistic

Prob.

C

493.6722

158.5363

3.113937

0.0082

X1

0.007337

0.001244

5.895809

0.0001

R-squared

0.727809

Meandependentvar

1136.720

AdjustedR-squared

0.706871

S.D.dependentvar

823.0463

S.E.ofregression

445.6086

Akaikeinfocriterion

15.16032

Sumsquaredresid

2581372.

Schwarzcriterion

15.25473

Loglikelihood

-111.7024

F-statistic

34.76056

Durbin-Watsonstat

1.327326

Prob（F-statistic）

0.000053

LeastSquares

09

-147.2516

151.9300

-0.969207

0.3501

X2

0.037776

0.003865

9.773950

0.0000

0.880218

0.871004

295.6060

14.33950

1135978.

14.43390

-105.5462

95.53011

1.389152

0.000000

-80.15322

211.5083

-0.378960

0.7108

X3

0.068291

0.010294

6.634063

0.771973

0.754432

407.8587

14.98328

2162534.

15.07769

-110.3746

44.01080

1.022370

0.000016

133.3664

117.5810

1.134251

0.2772

X4

0.012904

0.001209

10.67062

0.897526

0.889644

273.4151

14.18342

971825.5

14.27783

-104.3757

113.8620

1.716793

-194.0396

220.6236

-0.879505

0.3951

X5

0.637642

0.093473

6.821702

0.781643

0.764847

399.1167

14.93995

2070823.

15.03436

-110.0496

46.53562

0.974820

0.000012

11

1482.477

2347.499

0.631514

0.5387

X6

-3.285416

22.20766

-0.147941

0.8847

0.001681

-0.075113

853.3975

16.45989

9467734.

16.55430

-121.4492

0.021886

0.215522

0.884660

12

1337.975

621.3416

2.153364

0.0506

X7

-25.42163

73.42366

-0.346232

0.7347

0.009137

-0.067083

850.2046

16.45240

9397021.

16.54680

-121.3930

0.119877

0.246059

0.734708

从上述7个表可以看出，以X4为解释变量时，R2最大，但常数项不显著，就把不含常数项含X4的一元线性回归模型作为基本模型（所有不含常数项的一元线性回归模型中，此模型R2最大）

在此基础上分别加入X1,X2,X3,X5,X6,X7

21

0.010598

0.001061

9.988042

0.003000

0.000810

3.704267

0.0026

0.945213

0.940999

199.9192

13.55727

519580.1

13.65168

-99.67952

224.2834

1.190080

22

0.007698

0.002448

3.145342

0.0077

0.016101

0.006054

2.659477

0.0197

0.927066

0.921456

230.6650

13.84338

691682.2

13.93778

-101.8253

165.2434

1.886847

26

0.011413

0.002914

3.916365

0.0018

0.012656

0.013791

0.917717

0.3755

0.894237

0.886102

277.7686

14.21502

1003020.

14.30943

-104.6126

109.9165

1.648368

27

0.010146

0.002249

4.511236

0.0006

0.166624

0.092648

1.798469

0.0954

0.909822

0.902885

256.4876

14.05560

855216.8

14.15001

-103.4170

131.1597

1.546147

28

0.012874

0.001180

10.91316

1.310060

1.085108

1.207309

0.2488

0.898739

0.890950

271.7927

14.17152

960326.4

14.26593

-104.2864

115.3811

1.726650

0.013930

0.001168

11.92717

1.062945

13.41974

0.079208

0.9381

0.887440

0.878781

286.5559

14.27731

1067486.

14.37172

-105.0798

102.4936

1.705970

以X4、X1为解释变量的模型为基本回归模型，在此基础上分别加入x2、x3、x5、x6、x7得

33

197