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Ước lượng mức dự trữ ngoại hối tối ưu của Việt Nam - 36

Phụ lục 3.5.2. KIỂM ĐỊNH TÍNH DỪNG CỦA BIẾN open

Độ trễ tối ưu chọn theo tiêu chuẩn thông tin AIC nhỏ nhất là bậc 5 với AIC nhỏ nhất là -67.86557. Kết quả kiểm định ADF ở bậc 5 cho dạng phương trình bước ngẫu nhiên có hệ số chặn (random walk with drift) cho thấy p-value = 0.0166 < 5% nên giả thuyết H0 bị bác bỏ ở mức ý nghĩa 5% hay biến open là chuỗi dừng tại bậc 0: I(0).

. varsoc open, maxlag(8)


Selection-order criteria

Sample: 9 - 52 Number of obs = 44


lag

LL

LR

df

p

FPE

AIC

HQIC

SBIC

0

22.1627




.022375

-.96194

-.946902

-.92139

1

23.8295

3.3336

1

0.068

.021708

-.992249

-.962174

-.91115

2

23.8569

.05492

1

0.815

.022693

-.948043

-.90293

-.826394

3

24.0323

.35077

1

0.554

.023566

-.910561

-.850409

-.748362

4

28.5778

9.091

1

0.003

.020068

-1.07172

-.996531

-.868972

5

37.115

17.074*

1

0.000

.014257*

-1.41432*

-1.32409*

-1.17102*

6

38.0554

1.8808

1

0.170

.01431

-1.41161

-1.30635

-1.12776

7

38.0591

.0074

1

0.931

.014994

-1.36632

-1.24602

-1.04193

8

38.1949

.2715

1

0.602

.015622

-1.32704

-1.1917

-.962093

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Ước lượng mức dự trữ ngoại hối tối ưu của Việt Nam - 36

Endogenous: open Exogenous: _cons


Source

SS

df

MS

Model

.131363165

1

.131363165

Residual

.988141977

49

.020166163

Total

1.11950514

50

.022390103

. reg open l.open


Number of

obs =

51

F( 1,

49) =

6.51

Prob > F

=

0.0139

R-squared

=

0.1173

Adj R-squared = 0.0993 Root MSE = .14201


open

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

open







L1.

.3415147

.1338087

2.55

0.014

.0726161

.6104132

_cons

.564532

.1161136

4.86

0.000

.331193

.7978709


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

51

25.01708

28.19988

2

-52.39975

-48.5361

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

. reg open l.open l2.open


Source

SS

df

MS

Model

.124846757

2

.062423378

Residual

.976327938

47

.020772935

Total

1.1011747

49

.022472953

Number of obs = 50

F( 2, 47) = 3.01

Prob > F = 0.0591

R-squared = 0.1134

Adj R-squared = 0.0756

Root MSE = .14413



open

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

open







L1.

.3280953

.14521

2.26

0.029

.0359704

.6202201

L2.

.0160375

.1462109

0.11

0.913

-.278101

.310176

_cons

.5644859

.1437694

3.93

0.000

.2752591

.8537127


. estat ic


Akaike's information criterion and Bayesian information criterion



Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

50

24.44421

27.45257

3

-48.90513

-43.16907

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



. reg open l.open l2.open l3.open


Source

SS

df

MS

Model

.115033226

3

.038344409

Residual

.962333597

45

.021385191

Total

1.07736682

48

.022445142

Number of obs = 49

F( 3, 45) = 1.79

Prob > F = 0.1621

R-squared = 0.1068

Adj R-squared = 0.0472

Root MSE = .14624



open

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

open







L1.

.3171958

.1482659

2.14

0.038

.0185729

.6158187

L2.

.0266859

.1557307

0.17

0.865

-.2869718

.3403435

L3.

-.0460384

.1506682

-0.31

0.761

-.3494998

.2574229

_cons

.6059765

.169719

3.57

0.001

.2641448

.9478081


. estat ic


Akaike's information criterion and Bayesian information criterion



Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

49

23.99587

26.76226

4

-45.52453

-37.95725

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

. reg open l.open l2.open l3.open l4.open


Source

SS

df

MS

Model

.298303642

4

.074575911

Residual

.718980066

43

.016720467

Total

1.01728371

47

.021644334

Number of obs = 48

F( 4, 43) = 4.46

Prob > F = 0.0042

R-squared = 0.2932 Adj R-squared = 0.2275 Root MSE = .12931


open

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

open







L1.

.3088775

.1319776

2.34

0.024

.0427193

.5750357

L2.

.0041925

.1380497

0.03

0.976

-.2742112

.2825962

L3.

-.1815252

.1383273

-1.31

0.196

-.4604886

.0974383

L4.

.4824721

.1382081

3.49

0.001

.2037488

.7611954

_cons

.3457772

.1752424

1.97

0.055

-.0076328

.6991872


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

48

24.38851

32.71789

5

-55.43579

-46.07978

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



. reg open l.open l2.open l3.open l4.open l5.open


Source

SS

df

MS

Model

.510636999

5

.1021274

Residual

.503084564

41

.012270355

Total

1.01372156

46

.022037425

Number of obs = 47

F( 5, 41) = 8.32

Prob > F = 0.0000

R-squared = 0.5037 Adj R-squared = 0.4432 Root MSE = .11077


open

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

open







L1.

.570756

.1306394

4.37

0.000

.3069244

.8345875

L2.

-.0838778

.1202995

-0.70

0.490

-.3268276

.1590719

L3.

-.1699565

.1187802

-1.43

0.160

-.4098381

.0699251

L4.

.6581784

.1256154

5.24

0.000

.404493

.9118639

L5.

-.5633385

.1345353

-4.19

0.000

-.835038

-.2916389

_cons

.5094894

.16033

3.18

0.003

.1856963

.8332825


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

47

23.46809

39.93279

6

-67.86557

-56.76469

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

Source

SS

df

MS

Model

.532814071

6

.088802345

Residual

.473693756

39

.012145994

Total

1.00650783

45

.022366841

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


Number of

obs =

46

F( 6,

39) =

7.31

Prob > F

=

0.0000

R-squared

=

0.5294

Adj R-squared = 0.4570 Root MSE = .11021


open

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

open







L1.

.6978985

.1554304

4.49

0.000

.3835109

1.012286

L2.

-.2421942

.1573859

-1.54

0.132

-.5605373

.0761488

L3.

-.1354948

.1210609

-1.12

0.270

-.3803636

.109374

L4.

.6749864

.1261018

5.35

0.000

.4199215

.9300513

L5.

-.7008631

.1621922

-4.32

0.000

-1.028928

-.3727985

L6.

.2351153

.1601738

1.47

0.150

-.0888668

.5590974

_cons

.4119369

.1823885

2.26

0.030

.0430213

.7808525


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

46

22.63838

39.97305

7

-65.94609

-53.1456

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



. dfuller open, lags(5) drift reg


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

Test

1% Critical

5% Critical

10% Critical

Statistic

Value

Value

Value

Z(t) has t-distribution

Z(t) -2.207 -2.426 -1.685 -1.304

p-value for Z(t) = 0.0166



D.open

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

open







L1.

-.470552

.2131803

-2.21

0.033

-.90175

-.0393541

LD.

.1684505

.2020266

0.83

0.409

-.240187

.5770879

L2D.

-.0737438

.2023208

-0.36

0.717

-.4829762

.3354886

L3D.

-.2092386

.1720645

-1.22

0.231

-.5572719

.1387947

L4D.

.4657478

.1506541

3.09

0.004

.1610211

.7704746

L5D.

-.2351153

.1601738

-1.47

0.150

-.5590974

.0888668

_cons

.4119369

.1823885

2.26

0.030

.0430213

.7808525


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

Phụ lục 3.5.3. KIỂM ĐỊNH TÍNH DỪNG CỦA BIẾN fpiv

Độ trễ tối ưu chọn theo tiêu chuẩn thông tin AIC nhỏ nhất là bậc 7 với AIC nhỏ nhất là 45.41102. Kết quả kiểm định ADF ở bậc 7 cho dạng phương trình bước ngẫu nhiên có hệ số chặn (random walk with drift) cho thấy p-value = 0.0004 < 1% nên giả thuyết H0 bị bác bỏ ở mức ý nghĩa 1% hay biến fpiv là chuỗi dừng tại bậc 0: I(0).



. reg fpiv l.fpiv


Source

SS

df

MS

Model

988.456879

1

988.456879

Residual

78.2922461

49

1.59780094

Total

1066.74912

50

21.3349825


Number of

obs =

51

F( 1,

49) =

618.64

Prob > F

=

0.0000

R-squared

=

0.9266

Adj R-squared = 0.9251 Root MSE = 1.264


fpiv

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpiv







L1.

.9657367

.0388277

24.87

0.000

.8877096

1.043764

_cons

.0627071

.2534584

0.25

0.806

-.4466366

.5720507


. estat ic


Akaike's information criterion and Bayesian information criterion



Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

51

-149.8998

-83.29575

2

170.5915

174.4552

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

. reg fpiv l.fpiv l2.fpiv


Source

SS

df

MS

Model

961.5761

2

480.78805

Residual

11.6090264

47

.247000562

Total

973.185127

49

19.860921

Number of obs = 50

F( 2, 47) = 1946.51 Prob > F = 0.0000

R-squared = 0.9881 Adj R-squared = 0.9876 Root MSE = .49699


fpiv

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpiv







L1.

1.136824

.0562099

20.22

0.000

1.023745

1.249904

L2.

-.192484

.0564792

-3.41

0.001

-.3061055

-.0788626

_cons

.0244819

.1012874

0.24

0.810

-.1792822

.2282459


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

50

-145.1607

-34.44092

3

74.88185

80.61792

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



. reg fpiv l.fpiv l2.fpiv l3.fpiv


Source

SS

df

MS

Model

867.379601

3

289.126534

Residual

8.42258878

45

.18716864

Total

875.80219

48

18.245879

Number of obs = 49

F( 3, 45) = 1544.74 Prob > F = 0.0000

R-squared = 0.9904 Adj R-squared = 0.9897 Root MSE = .43263


fpiv

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpiv







L1.

1.486945

.1275456

11.66

0.000

1.230055

1.743835

L2.

-.4676932

.1528465

-3.06

0.004

-.7755419

-.1598444

L3.

-.0603262

.0549273

-1.10

0.278

-.1709554

.0503031

_cons

.0710774

.0897833

0.79

0.433

-.1097554

.2519102


. estat ic


Akaike's information criterion and Bayesian information criterion



Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

49

-140.1693

-26.38586

4

60.77173

68.33901

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

. reg fpiv l.fpiv l2.fpiv l3.fpiv l4.fpiv


Source

SS

df

MS

Model

766.464527

4

191.616132

Residual

7.89710391

43

.183653579

Total

774.361631

47

16.4757794

Number of obs = 48

F( 4, 43) = 1043.36 Prob > F = 0.0000

R-squared = 0.9898 Adj R-squared = 0.9889 Root MSE = .42855


fpiv

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpiv







L1.

1.546826

.1477346

10.47

0.000

1.248891

1.844761

L2.

-.4917724

.2534322

-1.94

0.059

-1.002867

.0193224

L3.

-.178715

.1668287

-1.07

0.290

-.515157

.1577271

L4.

.0920108

.0551443

1.67

0.102

-.0191983

.2032199

_cons

.0395561

.091323

0.43

0.667

-.1446143

.2237264


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

48

-134.8492

-24.79613

5

59.59226

68.94827

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


. reg fpiv l.fpiv l2.fpiv l3.fpiv l4.fpiv l5.fpiv


Source

SS

df

MS

Model

678.58358

5

135.716716

Residual

7.1390135

41

.17412228

Total

685.722593

46

14.9070129

Number of obs = 47

F( 5, 41) = 779.43

Prob > F = 0.0000

R-squared = 0.9896 Adj R-squared = 0.9883 Root MSE = .41728


fpiv

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpiv







L1.

1.578417

.148515

10.63

0.000

1.278485

1.878349

L2.

-.4469877

.2710623

-1.65

0.107

-.994409

.1004337

L3.

-.5245835

.2574035

-2.04

0.048

-1.04442

-.0047465

L4.

.4181495

.1654836

2.53

0.015

.0839486

.7523504

L5.

-.0648036

.0555008

-1.17

0.250

-.1768898

.0472827

_cons

.0330322

.0908412

0.36

0.718

-.1504253

.2164898


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

47

-129.6778

-22.40265

6

56.80529

67.90618

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

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


Source

SS

df

MS

Model

591.106116

6

98.5176859

Residual

5.91219234

39

.151594675

Total

597.018308

45

13.2670735

Number of obs =

46

F( 6, 39) =

649.88

Prob > F =

0.0000

R-squared =

0.9901

Adj R-squared =

0.9886

Root MSE =

.38935


fpiv

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpiv







L1.

1.450083

.1457796

9.95

0.000

1.155215

1.74495

L2.

-.209362

.2691121

-0.78

0.441

-.7536927

.3349687

L3.

-.502126

.2612084

-1.92

0.062

-1.03047

.026218

L4.

-.101943

.2553683

-0.40

0.692

-.6184741

.4145882

L5.

.3863452

.1681328

2.30

0.027

.0462646

.7264259

L6.

-.0775296

.0528628

-1.47

0.150

-.1844546

.0293954

_cons

.032393

.0864954

0.37

0.710

-.1425605

.2073465


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

46

-124.2272

-18.0838

7

50.16761

62.9681

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



Source

SS

df

MS

Model

504.532527

7

72.0760752

Residual

5.06504767

37

.13689318

Total

509.597574

44

11.581763

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


Number of

obs =

45

F( 7,

37) =

526.51

Prob > F

=

0.0000

R-squared

=

0.9901

Adj R-squared = 0.9882 Root MSE = .36999


fpiv

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

fpiv







L1.

1.30544

.1522674

8.57

0.000

.9969166

1.613962

L2.

-.0953018

.2606819

-0.37

0.717

-.6234934

.4328899

L3.

-.3319499

.2577072

-1.29

0.206

-.8541143

.1902144

L4.

-.2440203

.2644023

-0.92

0.362

-.7797503

.2917098

L5.

.0825279

.2511043

0.33

0.744

-.4262577

.5913135

L6.

.3149263

.1722848

1.83

0.076

-.0341558

.6640083

L7.

-.106801

.0516277

-2.07

0.046

-.2114086

-.0021934

_cons

.0489792

.0842967

0.58

0.565

-.1218221

.2197806


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

45

-118.4588

-14.70551

8

45.41102

59.86432

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

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