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Activité d'octroi de crédit et rentabilité des banques commerciales au Cameroun

( Télécharger le fichier original )
par Franklin DONGMO TSOBJIO
Université de Dschang Cameroun - Master en sciences économiques 2013
  

précédent sommaire suivant

Extinction Rebellion

ANNEXES

Annexe 1 : résultats de la stationnarité sortis de STATA Tableau 1 : test de stationnarité de DFA

Null Hypothesis: Unit root (individual unit root process)

Date: 04/27/13 Time: 09:33

Sample: 1 68

Series: TA, RTCSTD, RRSA, ROE, ROA, FPSTA, FPN,

ETI, DPTD, DBANC, CBANC

Exogenous variables: None

Automatic selection of maximum lags

Automatic selection of lags based on SIC: 0 to 7

Total number of observations: 701

Cross-sections included: 11 (1 dropped)

Method

Statistic

Prob.**

ADF - Fisher Chi-square

55.9648

0.0001

ADF - Choi Z-stat

-0.56722

0.2853

** Probabilities for Fisher tests are computed using an asympotic Chi

-square distribution. All other tests assume asymptotic

normality.

Intermediate ADF test results UNTITLED

Series

Prob.

Lag

Max Lag

Obs

TA

0.0733

6

10

61

RTCSTD

0.0512

1

10

66

RRSA

0.0654

1

10

66

ROE

0.0000

0

10

67

ROA

0.0063

1

10

66

FPSTA

0.0002

1

10

66

FPN

0.7557

6

10

61

ETI

0.6568

1

10

66

DPTD

0.0685

7

10

60

DBANC

0.9998

6

10

61

CBANC

0.0823

6

10

61

Null Hypothesis: Unit root (individual unit root process)

Date: 04/27/13 Time: 09:36

Sample: 1 68

Series: TA, RTCSTD, RRSA, ROE, ROA, FPSTA, FPN,

ETI, DPTD, DBANC, CBANC

Exogenous variables: Individual effects

Automatic selection of maximum lags

Automatic selection of lags based on SIC: 0 to 6

Total number of observations: 722

Cross-sections included: 11 (1 dropped)

Method

Statistic

Prob.**

78

ADF - Fisher Chi-square

184.089

0.0000

ADF - Choi Z-stat

-7.46930

0.0000

** Probabilities for Fisher tests are computed using an asympotic Chi

Null Hypothesis: Unit root (individual unit root process)

Date: 04/27/13 Time: 09:36

Sample: 1 68

Series: TA, RTCSTD, RRSA, ROE, ROA, FPSTA, FPN,

ETI, DPTD, DBANC, CBANC

Exogenous variables: Individual effects, individual linear trends

Automatic selection of maximum lags

Automatic selection of lags based on SIC: 0 to 1

Total number of observations: 735

Cross-sections included: 11 (1 dropped)

Method

Statistic

Prob.**

ADF - Fisher Chi-square

248.869

0.0000

ADF - Choi Z-stat

-13.0593

0.0000

** Probabilities for Fisher tests are computed using an asympotic Chi

-square distribution. All other tests assume asymptotic

normality.

Intermediate ADF test results UNTITLED

Series

Prob.

Lag

Max Lag

Obs

TA

0.0000

0

10

67

RTCSTD

0.0000

0

10

67

RRSA

0.0237

1

10

66

ROE

0.0013

0

10

67

ROA

0.0035

0

10

67

FPSTA

0.0000

0

10

67

FPN

0.0000

0

10

67

ETI

0.2974

1

10

66

DPTD

0.0001

0

10

67

DBANC

0.0000

0

10

67

CBANC

0.0000

0

10

67

-square distribution. All other tests assume asymptotic

normality.

Intermediate ADF test results UNTITLED

Series

Prob.

Lag

Max Lag

Obs

TA

0.0535

1

10

66

RTCSTD

0.0000

0

10

67

RRSA

0.0401

1

10

66

ROE

0.0004

0

10

67

ROA

0.0010

0

10

67

FPSTA

0.0000

0

10

67

FPN

0.0000

0

10

67

ETI

0.3694

1

10

66

DPTD

0.0000

0

10

67

DBANC

0.9993

6

10

61

CBANC

0.9801

6

10

61

79

Tableau 2 : test de stationnarité de Phillips Perron

Null Hypothesis: Unit root (individual unit root process)

Date: 04/27/13 Time: 09:37

Sample: 1 68

Series: TA, RTCSTD, RRSA, ROE, ROA,

FPSTA, FPN, ETI, DPTD, DBANC, CBANC

Exogenous variables: None

Newey-West bandwidth selection using Bartlett kernel

Total (balanced) observations: 737

Cross-sections included: 11 (1 dropped)

Method

Statistic

Prob.**

PP - Fisher Chi-square

163.411

0.0000

PP - Choi Z-stat

-7.77469

0.0000

** Probabilities for Fisher tests are computed using an

asympotic Chi-square distribution. All other tests

assume asymptotic normality.

Intermediate Phillips-Perron test results UNTITLED

Series

Prob.

Bandwidth

Obs

TA

0.0678

3.0

67

RTCSTD

0.1077

11.0

67

RRSA

0.0283

9.0

67

ROE

0.0000

2.0

67

ROA

0.0000

3.0

67

FPSTA

0.0000

4.0

67

FPN

0.0000

4.0

67

ETI

0.5973

2.0

67

DPTD

0.0005

5.0

67

DBANC

0.0139

13.0

67

CBANC

0.0249

0.0

67

Null Hypothesis: Unit root (individual unit root process)

Date: 04/27/13 Time: 09:41

Sample: 1 68

Series: TA, RTCSTD, RRSA, ROE, ROA,

FPSTA, FPN, ETI, DPTD, DBANC, CBANC

Exogenous variables: Individual effects

Newey-West bandwidth selection using Bartlett kernel

Total (balanced) observations: 737

Cross-sections included: 11 (1 dropped)

Method

Statistic

Prob.**

PP - Fisher Chi-square

228.630

0.0000

PP - Choi Z-stat

-11.7321

0.0000

** Probabilities for Fisher tests are computed using an

asympotic Chi-square distribution. All other tests

assume asymptotic normality.

Intermediate Phillips-Perron test results UNTITLED

Series

Prob.

Bandwidth

Obs

TA

0.0002

4.0

67

80

RTCSTD

0.0000

2.0

67

RRSA

0.0916

1.0

67

ROE

0.0004

2.0

67

ROA

0.0009

3.0

67

FPSTA

0.0000

0.0

67

FPN

0.0000

2.0

67

ETI

0.3164

2.0

67

DPTD

0.0000

4.0

67

DBANC

0.0590

1.0

67

CBANC

0.0000

4.0

67

Null Hypothesis: Unit root (individual unit root process)

Date: 04/27/13 Time: 09:42

Sample: 1 68

Series: TA, RTCSTD, RRSA, ROE, ROA,

FPSTA, FPN, ETI, DPTD, DBANC, CBANC

Exogenous variables: Individual effects, individual linear

Trends

Newey-West bandwidth selection using Bartlett kernel

Total (balanced) observations: 737

Cross-sections included: 11

Method

Statistic

Prob.**

PP - Fisher Chi-square

251.193

0.0000

PP - Choi Z-stat

-13.3844

0.0000

** Probabilities for Fisher tests are computed using an

asympotic Chi-square distribution. All other tests

assume asymptotic normality.

Intermediate Phillips-Perron test results UNTITLED

Series

Prob.

Bandwidth

Obs

TA

0.0000

4.0

67

RTCSTD

0.0000

2.0

67

RRSA

0.0000

3.0

67

ROE

0.0011

2.0

67

ROA

0.0028

3.0

67

FPSTA

0.0000

0.0

67

FPN

0.0000

17.0

67

ETI

0.2526

1.0

67

DPTD

0.0001

5.0

67

DBANC

0.0000

3.0

67

CBANC

0.0000

2.0

67

81

Tableau 3 : TEST DE DFA EN DIFFERENCE PREMIERE

Null Hypothesis: Unit root (individual unit root process)

Date: 05/01/13 Time: 07:38

Sample: 1 68

Series: TA, RTCSTD, RRSA, ROE, ROA, FPSTA, FPN,

ETI, DPTD, DBANC, CBANC

Exogenous variables: None

Automatic selection of maximum lags

Automatic selection of lags based on SIC: 0 to 5

Total number of observations: 715

Cross-sections included: 11

Method

Statistic

Prob.**

ADF - Fisher Chi-square

2421.85

0.0000

ADF - Choi Z-stat

-47.1423

0.0000

** Probabilities for Fisher tests are computed using an asympotic Chi

-square distribution. All other tests assume asymptotic

normality.

Intermediate ADF test results D(UNTITLED)

Series

Prob.

Lag

Max Lag

Obs

D(TA)

0.0000

0

10

66

D(RTCSTD)

0.0000

0

10

66

D(RRSA)

0.0000

0

10

66

D(ROE)

0.0000

0

10

66

D(ROA)

0.0000

0

10

66

D(FPSTA)

0.0000

1

10

65

D(FPN)

0.0000

5

10

61

D(ETI)

0.0000

0

10

66

D(DPTD)

0.0000

0

10

66

D(DBANC)

0.0000

0

10

66

D(CBANC)

0.0000

5

10

61

Tableau 4 : TEST DE Phillips Perron EN DIFFERENCE PREMIERE

Null Hypothesis: Unit root (individual unit root process)

Date: 05/01/13 Time: 07:40

Sample: 1 68

Series: TA, RTCSTD, RRSA, ROE, ROA,

FPSTA, FPN, ETI, DPTD, DBANC, CBANC

Exogenous variables: None

Newey-West bandwidth selection using Bartlett kernel

Total (balanced) observations: 726

Cross-sections included: 11

Method

Statistic

Prob.**

PP - Fisher Chi-square

2897.30

0.0000

PP - Choi Z-stat

-53.0660

0.0000

** Probabilities for Fisher tests are computed using an

asympotic Chi-square distribution. All other tests

assume asymptotic normality.

Intermediate Phillips-Perron test results D(UNTITLED)

Series

Prob.

Bandwidth

Obs

D(TA)

0.0000

19.0

66

82

D(RTCSTD)

0.0000

12.0

66

D(RRSA)

0.0000

10.0

66

D(ROE)

0.0000

5.0

66

D(ROA)

0.0000

4.0

66

D(FPSTA)

0.0000

47.0

66

D(FPN)

0.0000

65.0

66

D(ETI)

0.0000

2.0

66

D(DPTD)

0.0000

38.0

66

D(DBANC)

0.0000

16.0

66

D(CBANC)

0.0000

65.0

66

Annexe 2 : résultats des régressions par les MCO et des tests d'autocorrélation et d'hétéroscédasticité

Tableau 1 : résultats des régressions par les MCO, des tests d'autocorrélation et d'hétéroscédasticité et la régression par les MCG du modèle ROE.

Equation de ROE

df MS

8 1474.6534

59 121.141

67 282.75442

Number of obs = 68

F( 8, 59) = 12.17

Prob > F = 0.0000

R-squared = 0.6227

Adj R-squared = 0.5716

Root MSE = 11.006

Regression par les MCO

Source | SS

+

Model | 11797.2272

Residual | 7147.319

+

Total | 18944.5462

roe | Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpsta |

.3291626

.3588413

0.92

0.363

-.3888772

1.047203

rrsa |

2.294563

.8906147

2.58

0.013

.5124471

4.076679

ta |

.0000238

.0000236

1.01

0.318

-.0000235

.0000711

dptd |

.0700445

.1692866

0.41

0.681

-.2686971

.4087861

fpn |

.0004475

.0001753

2.55

0.013

.0000968

.0007982

rtcstd |

-126.1769

75.76681

-1.67

0.101

-277.7859

25.43218

cbanc |

-.0001542

.0000866

-1.78

0.080

-.0003275

.0000191

dbanc |

-.0000273

.0000465

-0.59

0.559

-.0001203

.0000657

_ cons | 17.36273

13.66144

1.27

0.209

-9.973761

44.69921

Test d'heteroscedasticité

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance

Variables:

fitted values of roe

chi2(1)

=

19.09

Prob > chi2

=

0.0000

Test d'heteroscedasticité à travers le VIF

Variable

|

VIF

1/VIF

+

cbanc

|

60.38

0.016561

dbanc

|

32.87

0.030427

fpn

|

24.92

0.040120

ta |

16.63

0.060120

rrsa

|

6.35

0.157357

rtcstd

|

4.15

0.241239

dptd

|

1.82

0.549482

fpsta

|

1.15

0.870648

+

Mean VIF

|

18.53

Test d'heteroscedasticité conditionnelle

LM test for autoregressive conditional heteroskedasticity (ARCH)

lags(p) | chi2 df Prob > chi2

+

1 | 38.981 1 0.0000

H0: no ARCH effects vs. H1: ARCH(p) disturbance

Test d'autocorrelation de Breusch godfrey

83

84

Breusch-Godfrey LM test for autocorrelation

lags(p) | chi2 df Prob > chi2

+

1 | 47.769 1 0.0000

H0: no serial correlation

Test d'autocorrelation de durbin Watson

Durbin's alternative test for autocorrelation

lags(p) | chi2 df Prob > chi2

+

1 | 136.947 1 0.0000

H0: no serial correlation

Determination de la valeur de AIC

Model | Obs ll(null) ll(model) df AIC BIC

+

. | 68 -287.8998 -254.7573 9 527.5146 547.4902

Note: N=Obs used in calculating BIC; see [R] BIC note Correction de l'heteroscedasticité par la procedure de white

Linear regression Number of obs = 68

F( 8, 59) = 12.57

Prob > F = 0.0000

R-squared = 0.6227

Root MSE = 11.006

|

roe |

fpsta | rrsa |

ta |

+

Robust

Coef. Std. Err. t

.3291626 .1498602 2.20

2.294563 .9915612 2.31

.0000238 .0000135 1.77

P>|t| [95% Conf.

0.032 .029293

0.024 .3104536

0.082 -3.14e-06

Interval]

.6290323

4.278672

.0000508

dptd |

.0700445 .3119628 0.22

0.823 -.5541917

.6942806

fpn |

.0004475 .0001957 2.29

0.026 .0000559

.0008391

rtcstd |

-126.1769 76.12553 -1.66

0.103 -278.5037

26.14999

cbanc |

-.0001542 .0001275 -1.21

0.231 -.0004093

.0001009

dbanc |

-.0000273 .00004 -0.68

0.498 -.0001074

.0000528

_ cons | 17.36273 11.40456 1.52

0.133 -5.457752

40.18321

Estimation par la method moindres carrées generalisées

Iteration 0: log likelihood = -254.75731

Generalized linear models

No. of obs

=

68

Optimization : ML

Residual df

=

59

Scale parameter

=

121.141

Deviance = 7147.319001

(1/df) Deviance

=

121.141

Pearson = 7147.319001

(1/df) Pearson

=

121.141

85

Variance function: V(u) = 1 [Gaussian]

Link function : g(u) = u [Identity]

AIC = 7.757568

Log likelihood = -254.7573067 BIC = 6898.368

| OIM

roe | Coef. Std. Err. z P>|z| [95% Conf. Interval]

+

fpsta | .3291626 .3588413 0.92 0.359 -.3741535 1.032479

rrsa | 2.294563 .8906147 2.58 0.010 .5489903 4.040136

ta | .0000238 .0000236 1.01 0.314 -.0000225 .0000701

dptd | .0700445 .1692866 0.41 0.679 -.2617511 .40184

fpn | .0004475 .0001753 2.55 0.011 .000104 .000791

rtcstd | -126.1769 75.76681 -1.67 0.096 -274.6771 22.32336

cbanc | -.0001542 .0000866 -1.78 0.075 -.0003239 .0000156

dbanc | -.0000273 .0000465 -0.59 0.557 -.0001184 .0000638

cons | 17.36273 13.66144 1.27 0.204 -9.413213 44.13867

_

Test de normalité des residus

Shapiro-Wilk W test for normal data

Variable | Obs W V z Prob>z

+

resid | 68 0.99270 0.439 -1.787 0.96300

86

Annexe 3: résultats des régressions par les MCO, des tests d'autocorrélation et d'hétéroscédasticité et la régression par les MCG du modèle ROA.

Equation de ROA

df MS

Number of obs

F( 8, 59)

= 68

= 12.09

Regression par les MCO

Source |

+

SS

Model |

18.5175555

8 2.31469444

Prob > F

= 0.0000

Residual |

11.296186

59 .19146078

R-squared

= 0.6211

+

Adj R-squared

= 0.5697

Total |

29.8137416

67 .444981218

Root MSE

= .43756

roa |

Coef.

Std. Err. t

P>|t|

[95% Conf.

Interval]

+

fpsta |

.012863

.0142658 0.90

0.371

-.0156828

.0414089

rrsa |

.1006643

.0354066 2.84

0.006

.0298159

.1715128

ta |

-1.15e-07

9.39e-07 -0.12

0.903

-1.99e-06 1.76e-06

dptd |

.002146

.00673 0.32

0.751

-.0113208

.0156127

fpn |

.000012

6.97e-06 1.73

0.090

-1.92e-06

.000026

rtcstd |

-9.129399

3.012127 -3.03

0.004

-15.15665

-3.102146

cbanc |

-3.21e-06

3.44e-06 -0.93

0.356

-.0000101

3.68e-06

dbanc |

-1.55e-06

1.85e-06 -0.84

0.404

-5.25e-06

2.14e-06

_cons |

1.58716

.543114 2.92

0.005

.5003917

2.673929

Test d'heteroscedasticité

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance

Variables: fitted values of roa

chi2(1) = 13.37

Prob > chi2 = 0.0003

Test d'heteroscedasticité à travers le VIF

Variable

|

VIF

1/VIF

cbanc

|

60.38

0.016561

dbanc

|

32.87

0.030427

fpn

|

24.92

0.040120

ta |

16.63

0.060120

rrsa

|

6.35

0.157357

rtcstd |

4.15

0.241239

dptd |

1.82

0.549482

fpsta |

1.15

0.870648

+

Mean VIF |

18.53

Test d'heteroscedasticité conditionnelle

LM test for autoregressive conditional heteroskedasticity (ARCH)

lags(p) | chi2 df Prob > chi2

+

1 | 40.732 1 0.0000

H0: no ARCH effects vs. H1: ARCH(p) disturbance

Test d'autocorrelation de Breusch godfrey

Breusch-Godfrey LM test for autocorrelation

lags(p) | chi2 df Prob > chi2

+

1 | 52.830 1 0.0000

H0: no serial correlation

Test d'autocorrelation de durbin Watson

Durbin's alternative test for autocorrelation

lags(p) | chi2 df Prob > chi2

+

1 | 201.978 1 0.0000

H0: no serial correlation

Determination de la valeur de AIC

Model | Obs ll(null) ll(model) df AIC BIC

+

. | 68 -287.8998 -254.7573 9 527.5146 547.4902

Note: N=Obs used in calculating BIC; see [R] BIC note

=

68

=

13.26

=

0.0000

=

0.6211

=

.43756

87

Correction de l'heteroscedasticité par la procedure de white

Linear regression Number of obs

F( 8, 59) Prob > F R-squared Root MSE

88

roa

|

|

Coef.

Robust

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpsta

|

.012863

.0056025

2.30

0.025

.0016524

.0240737

rrsa

|

.1006643

.0371351

2.71

0.009

.0263571

.1749716

ta |

-1.15e-07

5.05e-07

-0.23

0.820

-1.13e-06

8.95e-07

dptd

|

.002146

.0118866

0.18

0.857

-.0216391

.0259311

fpn

|

.000012

7.57e-06

1.59

0.117

-3.11e-06

.0000272

rtcstd

|

-9.129399

2.750259

-3.32

0.002

-14.63265

-3.626143

cbanc

|

-3.21e-06

4.85e-06

-0.66

0.511

-.0000129

6.50e-06

dbanc

|

-1.55e-06

1.65e-06

-0.94

0.351

-4.86e-06

1.75e-06

_cons

|

1.58716

.4185245

3.79

0.000

.7496946

2.424626

Estimation par la method moindres carrées generalisées

Iteration 0: log likelihood = -35.456373

Generalized linear models

Optimization : ML

No. of obs =

Residual df =

68

59

Scale parameter =

.1914608

Deviance

= 11.29618604

(1/df) Deviance =

.1914608

Pearson

= 11.29618604

(1/df) Pearson =

.1914608

Variance function: V(u) =

1

[Gaussian]

Link function

: g(u) =

u

[Identity]

AIC =

1.30754

Log likelihood

= -35.4563734

BIC =

-237.6548

|

OIM

roa

|

Coef.

Std. Err.

z

P>|z| [95% Conf.

Interval]

fpsta

|

.012863

.0142658

0.90

0.367 -.0150975

.0408235

rrsa

|

.1006643

.0354066

2.84

0.004 .0312687

.17006

ta |

-1.15e-07

9.39e-07

-0.12

0.902 -1.96e-06

1.73e-06

dptd

|

.002146

.00673

0.32

0.750 -.0110446

.0153366

fpn

|

.000012

6.97e-06

1.73

0.084 -1.63e-06

.0000257

rtcstd

|

-9.129399

3.012127

-3.03

0.002 -15.03306

-3.225738

cbanc

|

-3.21e-06

3.44e-06

-0.93

0.352 -9.95e-06

3.54e-06

dbanc

|

-1.55e-06

1.85e-06

-0.84

0.400 -5.17e-06

2.07e-06

_cons

|

1.58716

.543114

2.92

0.003 .5226764

2.651644

Test de normalité des residus

Shapiro-Wilk W test for normal data

Variable | Obs W V z Prob>z

+

.

resid2 | 68 0.98853 0.690 -0.807 0.79007

89

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