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L'impact de l'augmentation du prix du petrole sur la hausse des prix: Cas de la Tunisie

( Télécharger le fichier original )
par Slah Bahloul
faculté des sciences économiques et de gestion de sfax - Mastère de recherche en Finance 2008
  

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Annexe V

Estimation du modèle VAR(5) pour les séries mensuelles :

Vector Autoregression Estimates

Date: 09/25/07 Time: 15:57

Sample(adjusted): 1974:04 2007:02

Included observations: 395 after adjusting

endpoints

Standard errors in ( ) & t-statistics in [ ]

 

D(INFM)

D(LPPM)

D(INFM(-1))

-0.737650

0.000599

 

(0.04779)

(0.00427)

 

[-15.4339]

[ 0.14032]

D(INFM(-2))

-0.466546

-0.001034

 

(0.05419)

(0.00484)

 

[-8.60993]

[-0.21364]

D(INFM(-3))

-0.461900

-0.001030

 

(0.05224)

(0.00467)

 

[-8.84215]

[-0.22076]

D(INFM(-4))

-0.268127

0.000261

 

(0.05102)

(0.00456)

 

[-5.25528]

[ 0.05730]

D(INFM(-5))

-0.245578

-0.000310

 

(0.04263)

(0.00381)

 

[-5.76125]

[-0.08145]

D(LPPM(-1))

0.132130

0.515062

 

(0.57198)

(0.05109)

 

[ 0.23101]

[ 10.0818]

D(LPPM(-2))

-0.279399

-0.097007

 

(0.64008)

(0.05717)

 

[-0.43651]

[-1.69680]

D(LPPM(-3))

-0.059258

0.009601

 

(0.62095)

(0.05546)

 

[-0.09543]

[ 0.17311]

D(LPPM(-4))

0.462892

-0.138268

 

(0.61245)

(0.05470)

 

[ 0.75581]

[-2.52764]

D(LPPM(-5))

-0.370285

0.067562

 

(0.53659)

(0.04793)

 

[-0.69007]

[ 1.40968]

C

0.001723

0.002539

 

(0.03364)

(0.00300)

 

[ 0.05122]

[ 0.84525]

R-squared

0.450381

0.241885

Adj. R-squared

0.436068

0.222142

Sum sq. resids

168.9492

1.347830

S.E. equation

0.663304

0.059245

F-statistic

31.46653

12.25193

Log likelihood

-392.7464

561.3963

Akaike AIC

2.044286

-2.786817

Schwarz SC

2.155090

-2.676012

Mean dependent

0.007529

0.003793

S.D. dependent

0.883281

0.067174

Determinant Residual Covariance

0.001534

Log Likelihood (d.f. adjusted)

158.7929

Akaike Information Criteria

-0.692622

Schwarz Criteria

-0.471013

Fonction de réponse impulsionnelle pour les séries mensuelles :

Décomposition de la variance pour les séries mensuelles :

Variance Decomposition of D(INFM):

Period

S.E.

D(INFM)

D(LPPM)

1

0.663304

100.0000

0.000000

2

0.823902

99.99103

0.008968

3

0.825621

99.94229

0.057714

4

0.833784

99.94330

0.056699

5

0.841810

99.82846

0.171536

6

0.846341

99.74636

0.253641

7

0.863025

99.75502

0.244978

8

0.871110

99.75955

0.240452

9

0.872315

99.75997

0.240028

10

0.874499

99.76116

0.238839

11

0.876214

99.75638

0.243622

12

0.877241

99.75378

0.246219

13

0.878720

99.75439

0.245606

14

0.879533

99.75450

0.245495

15

0.879865

99.75459

0.245405

16

0.880244

99.75472

0.245277

17

0.880519

99.75448

0.245516

18

0.880697

99.75441

0.245590

19

0.880872

99.75446

0.245542

20

0.880981

99.75445

0.245547

21

0.881045

99.75446

0.245544

22

0.881104

99.75446

0.245538

23

0.881146

99.75445

0.245551

24

0.881174

99.75445

0.245554

25

0.881198

99.75445

0.245551

26

0.881214

99.75445

0.245552

27

0.881225

99.75445

0.245552

28

0.881234

99.75445

0.245552

29

0.881240

99.75445

0.245553

30

0.881244

99.75445

0.245553

31

0.881248

99.75445

0.245553

32

0.881250

99.75445

0.245553

33

0.881252

99.75445

0.245553

34

0.881253

99.75445

0.245553

35

0.881254

99.75445

0.245553

36

0.881255

99.75445

0.245553

Cholesky Ordering: D(INFM) D(LPPM)

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