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Ước lượng mức dự trữ ngoại hối tối ưu của Việt Nam - 32

Source

SS

df

MS

Model

.353487001

1

.353487001

Residual

.386953973

49

.00789702

Total

.740440974

50

.014808819

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Ước lượng mức dự trữ ngoại hối tối ưu của Việt Nam - 32

. reg fpi l.fpi


Number of

obs =

51

F( 1,

49) =

44.76

Prob > F

=

0.0000

R-squared

=

0.4774

Adj R-squared = 0.4667 Root MSE = .08887


fpi

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpi







L1.

.6597397

.0986092

6.69

0.000

.4615771

.8579023

_cons

.1109946

.0334904

3.31

0.002

.0436931

.1782961


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

51

35.55868

52.10665

2

-100.2133

-96.34965

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


Source

SS

df

MS

Model

.330410892

2

.165205446

Residual

.321853601

47

.006847949

Total

.652264493

49

.01331152

. reg fpi l.fpi l2.fpi


Number of

obs =

50

F( 2,

47) =

24.12

Prob > F

=

0.0000

R-squared

=

0.5066

Adj R-squared = 0.4856 Root MSE = .08275


fpi

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpi







L1.

.3616871

.1341023

2.70

0.010

.0919082

.6314661

L2.

.3505302

.1272822

2.75

0.008

.0944715

.6065889

_cons

.0980804

.0347965

2.82

0.007

.0280788

.168082


. estat ic


Akaike's information criterion and Bayesian information criterion



Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

50

37.53628

55.19511

3

-104.3902

-98.65415

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

. reg fpi l.fpi l2.fpi l3.fpi


Source

SS

df

MS

Model

.24993596

3

.083311987

Residual

.311164032

45

.006914756

Total

.561099993

48

.011689583

Number of obs = 49

F( 3, 45) = 12.05

Prob > F = 0.0000

R-squared = 0.4454 Adj R-squared = 0.4085 Root MSE = .08316


fpi

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpi







L1.

.3464991

.148513

2.33

0.024

.0473786

.6456197

L2.

.3523675

.1448103

2.43

0.019

.0607047

.6440304

L3.

-.0399731

.1384993

-0.29

0.774

-.3189249

.2389788

_cons

.1169749

.0381268

3.07

0.004

.0401835

.1937663


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

49

39.97908

54.42377

4

-100.8475

-93.28026

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



. reg fpi l.fpi l2.fpi l3.fpi l4.fpi


Source

SS

df

MS

Model

.273346706

4

.068336676

Residual

.221373597

43

.005148223

Total

.494720303

47

.010525964

Number of obs = 48

F( 4, 43) = 13.27

Prob > F = 0.0000

R-squared = 0.5525 Adj R-squared = 0.5109 Root MSE = .07175


fpi

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpi







L1.

.3367711

.1304304

2.58

0.013

.0737331

.5998091

L2.

.1303874

.1358301

0.96

0.342

-.14354

.4043149

L3.

-.2809571

.1329846

-2.11

0.040

-.5491461

-.0127681

L4.

.4918838

.1205174

4.08

0.000

.2488373

.7349304

_cons

.1169279

.0364901

3.20

0.003

.0433386

.1905173


. estat ic


Akaike's information criterion and Bayesian information criterion



Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

48

41.69008

60.98946

5

-111.9789

-102.6229

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

. reg fpi l.fpi l2.fpi l3.fpi l4.fpi l5.fpi

Source

SS

df

MS

Model

.328867639

5

.065773528

Residual

.118506819

41

.00289041

Total

.447374458

46

.009725532

Number of obs = 47

F( 5, 41) = 22.76

Prob > F = 0.0000

R-squared = 0.7351 Adj R-squared = 0.7028 Root MSE = .05376


fpi

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpi







L1.

.6779581

.1147951

5.91

0.000

.4461248

.9097914

L2.

-.0150693

.1053265

-0.14

0.887

-.2277805

.1976419

L3.

-.137216

.1029928

-1.33

0.190

-.3452141

.0707821

L4.

.8176058

.1059318

7.72

0.000

.6036722

1.031539

L5.

-.6330717

.1068721

-5.92

0.000

-.8489043

-.4172392

_cons

.0993924

.0306731

3.24

0.002

.0374468

.161338


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

47

42.6908

73.9088

6

-135.8176

-124.7167

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



Source

SS

df

MS

Model

.280370619

6

.046728437

Residual

.113003827

39

.002897534

Total

.393374446

45

.008741654

. reg fpi l.fpi l2.fpi l3.fpi l4.fpi l5.fpi l6.fpi


Number of

obs =

46

F( 6,

39) =

16.13

Prob > F

=

0.0000

R-squared

=

0.7127

Adj R-squared = 0.6685 Root MSE = .05383


fpi

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpi







L1.

.6909409

.1567796

4.41

0.000

.3738243

1.008057

L2.

-.0560839

.1565813

-0.36

0.722

-.3727995

.2606317

L3.

-.150728

.1055843

-1.43

0.161

-.3642924

.0628365

L4.

.8045741

.1065059

7.55

0.000

.5891457

1.020003

L5.

-.6627043

.1664395

-3.98

0.000

-.99936

-.3260487

L6.

.035994

.1457704

0.25

0.806

-.2588545

.3308425

_cons

.1172268

.0347088

3.38

0.002

.0470216

.187432


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

46

44.24643

72.93525

7

-131.8705

-119.07

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

Source

SS

df

MS

Model

.241193621

7

.034456232

Residual

.100939569

37

.002728096

Total

.34213319

44

.007775754

. reg fpi l.fpi l2.fpi l3.fpi l4.fpi l5.fpi l6.fpi l7.fpi


Number of

obs =

45

F( 7,

37) =

12.63

Prob > F

=

0.0000

R-squared

=

0.7050

Adj R-squared = 0.6492 Root MSE = .05223


fpi

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpi







L1.

.6361472

.1559393

4.08

0.000

.3201841

.9521103

L2.

.136589

.1862101

0.73

0.468

-.2407085

.5138866

L3.

-.3483831

.1521878

-2.29

0.028

-.6567448

-.0400214

L4.

.8116392

.1061729

7.64

0.000

.5965124

1.026766

L5.

-.638647

.1624486

-3.93

0.000

-.9677991

-.3094948

L6.

-.2007242

.1916521

-1.05

0.302

-.5890484

.1875999

L7.

.2342672

.1416973

1.65

0.107

-.0528389

.5213732

_cons

.1305104

.038625

3.38

0.002

.0522488

.2087721


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

45

45.93016

73.39542

8

-130.7908

-116.3375

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


. dfuller fpi, lags(5) drift reg


Augmented Dickey-Fuller test for unit root Number of obs = 46

Z(t) has t-distribution

Test 1% Critical 5% Critical 10% Critical Statistic Value Value Value

Z(t) -3.389 -2.426 -1.685 -1.304

p-value for Z(t) = 0.0008


D.fpi

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpi







L1.

-.3380072

.0997287

-3.39

0.002

-.5397275

-.1362868

LD.

.0289481

.1419019

0.20

0.839

-.2580757

.3159718

L2D.

-.0271358

.1119722

-0.24

0.810

-.253621

.1993493

L3D.

-.1778638

.1054143

-1.69

0.100

-.3910843

.0353568

L4D.

.6267104

.1085646

5.77

0.000

.4071176

.8463031

L5D.

-.035994

.1457704

-0.25

0.806

-.3308425

.2588545

_cons

.1172268

.0347088

3.38

0.002

.0470216

.187432


Nguồn : Tác giả xử lý và copy từ phần mềm Stata 13.0

Phụ lục 2.5.6. KIỂM ĐỊNH TÍNH DỪNG CỦA BIẾN erv

Độ trễ tối ưu chọn theo tiêu chuẩn thông tin AIC nhỏ nhất là bậc 1 với AIC nhỏ nhất là -283.9034. Kết quả kiểm định ADF ở bậc 1 cho dạng phương trình bước ngẫu nhiên có hệ số chặn (random walk with drift) cho thấy p-value = 0.0840 < 10% nên giả thuyết H0 bị bác bỏ ở mức ý nghĩa 10% hay biến erv là chuỗi dừng tại bậc 0: I(0).


. varsoc erv, maxlag(8)


Selection-order criteria

Sample: 9 - 52 Number of obs = 44


lag

LL

LR

df

p

FPE

AIC

HQIC

SBIC

0

40.7433




.009615

-1.80651

-1.79148

-1.76597

1

121.835

162.18*

1

0.000

.000252*

-5.44703*

-5.41695*

-5.36593*

2

121.846

.02363

1

0.878

.000264

-5.40211

-5.357

-5.28046

3

123.163

2.6332

1

0.105

.00026

-5.4165

-5.35635

-5.2543

4

123.596

.86565

1

0.352

.000267

-5.39072

-5.31553

-5.18797

5

125.433

3.6746

1

0.055

.000257

-5.42878

-5.33855

-5.18548

6

126.661

2.4553

1

0.117

.000255

-5.43913

-5.33386

-5.15528

7

126.673

.02386

1

0.877

.000267

-5.39421

-5.27391

-5.06982

8

127.03

.71422

1

0.398

.000275

-5.36499

-5.22965

-5.00004

Endogenous: erv Exogenous: _cons


.

. reg erv l.erv


Source

SS

df

MS

Model

.568678806

1

.568678806

Residual

.01055437

49

.000215395

Total

.579233176

50

.011584664

Number of obs = 51

F( 1, 49) = 2640.16 Prob > F = 0.0000

R-squared = 0.9818 Adj R-squared = 0.9814 Root MSE = .01468


erv

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

erv







L1.

.9745262

.0189661

51.38

0.000

.9364124

1.01264

_cons

-.0004754

.0033687

-0.14

0.888

-.0072452

.0062943


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

51

41.81997

143.9517

2

-283.9034

-280.0397

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

.



. reg erv l.erv l2.erv


Source

SS

df

MS

Model

.544905141

2

.272452571

Residual

.010487747

47

.000223144

Total

.555392888

49

.011334549

Number of obs = 50

F( 2, 47) = 1220.97 Prob > F = 0.0000

R-squared = 0.9811 Adj R-squared = 0.9803 Root MSE = .01494


erv

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

erv







L1.

.9687667

.1485076

6.52

0.000

.6700079

1.267526

L2.

.0036547

.1464198

0.02

0.980

-.2909039

.2982133

_cons

-.0003604

.0034812

-0.10

0.918

-.0073636

.0066429


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

50

41.55564

140.7923

3

-275.5847

-269.8486

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

.

Source

SS

df

MS

Model

.521440924

3

.173813641

Residual

.009774147

45

.000217203

Total

.531215071

48

.011066981

. reg erv l.erv l2.erv l3.erv


Number of

obs =

49

F( 3,

45) =

800.23

Prob > F

=

0.0000

R-squared

=

0.9816

Adj R-squared = 0.9804 Root MSE = .01474


erv

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

erv







L1.

.9665302

.147013

6.57

0.000

.6704307

1.26263

L2.

.2594563

.203821

1.27

0.210

-.1510603

.6699729

L3.

-.2540697

.1445778

-1.76

0.086

-.5452643

.037125

_cons

.0007389

.0034884

0.21

0.833

-.0062871

.0077649


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

49

41.32002

139.208

4

-270.4159

-262.8486

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

.


. dfuller erv, lags(1) drift reg


Augmented Dickey-Fuller test for unit root Number of obs = 50

Z(t) has t-distribution

Test 1% Critical 5% Critical 10% Critical Statistic Value Value Value

Z(t) -1.400 -2.408 -1.678 -1.300

p-value for Z(t) = 0.0840


D.erv

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

erv







L1.

-.0275786

.019694

-1.40

0.168

-.0671979

.0120406

LD.

-.0036547

.1464198

-0.02

0.980

-.2982133

.2909039

_cons

-.0003604

.0034812

-0.10

0.918

-.0073636

.0066429


.


Nguồn : Tác giả xử lý và copy từ phần mềm Stata 13.0

Phụ lục 2.5.7. KIỂM ĐỊNH TÍNH DỪNG CỦA BIẾN cost

Độ trễ tối ưu chọn theo tiêu chuẩn thông tin AIC nhỏ nhất là bậc 3 với AIC nhỏ nhất là -283.1703. Kết quả kiểm định ADF ở bậc 3 cho dạng phương trình bước ngẫu nhiên có hệ số chặn (random walk with drift) cho thấy p-value = 0.0647 < 10% nên giả thuyết H0 bị bác bỏ ở mức ý nghĩa 10% hay biến cost là chuỗi dừng tại bậc 0: I(0).


. varsoc cost, maxlag(8)


Selection-order criteria

Sample: 9 - 52 Number of obs = 44


lag

LL

LR

df

p

FPE

AIC

HQIC

SBIC

0

84.8716




.001294

-3.81235

-3.79731

-3.7718

1

119.87

69.996

1

0.000

.000276

-5.35771

-5.32763

-5.27661

2

124.257

8.7755

1

0.003

.000237

-5.5117

-5.46658

-5.39005

3

128.405

8.2952*

1

0.004

.000205*

-5.65477*

-5.59462*

-5.49257*

4

129.248

1.6862

1

0.194

.000207

-5.64764

-5.57245

-5.44489

5

129.764

1.0323

1

0.310

.000211

-5.62565

-5.53542

-5.38235

6

129.774

.0189

1

0.891

.000221

-5.58062

-5.47536

-5.29677

7

129.832

.11754

1

0.732

.000231

-5.53784

-5.41754

-5.21344

8

129.918

.17173

1

0.679

.000242

-5.49629

-5.36095

-5.13134

Endogenous: cost Exogenous: _cons

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