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Ước lượng mức dự trữ ngoại hối tối ưu của Việt Nam - 31

. reg open l.open


Source

SS

df

MS

Model

.131581389

1

.131581389

Residual

.987717395

49

.020157498

Total

1.11929878

50

.022385976

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Ước lượng mức dự trữ ngoại hối tối ưu của Việt Nam - 31

Number of obs = 51

F( 1, 49) = 6.53

Prob > F = 0.0138

R-squared = 0.1176

Adj R-squared = 0.0995

Root MSE = .14198


open

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

open







L1.

.3418241

.13379

2.55

0.014

.072963

.6106852

_cons

.5642574

.1160949

4.86

0.000

.330956

.7975588


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

51

25.02178

28.21083

2

-52.42167

-48.55802

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



. reg open l.open l2.open


Source

SS

df

MS

Model

.125025232

2

.062512616

Residual

.975824188

47

.020762217

Total

1.10084942

49

.022466315

Number of obs = 50

F( 2, 47) = 3.01

Prob > F = 0.0588

R-squared = 0.1136

Adj R-squared = 0.0759

Root MSE = .14409



open

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

open







L1.

.3282823

.1452054

2.26

0.028

.0361667

.6203978

L2.

.0162378

.1462028

0.11

0.912

-.2778842

.3103598

_cons

.5641522

.1437236

3.93

0.000

.2750176

.8532869


. estat ic


Akaike's information criterion and Bayesian information criterion



Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

50

24.4516

27.46547

3

-48.93094

-43.19487

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

Source

SS

df

MS

Model

.115162179

3

.038387393

Residual

.961744261

45

.021372095

Total

1.07690644

48

.022435551

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


Number of

obs =

49

F( 3,

45) =

1.80

Prob > F

=

0.1615

R-squared

=

0.1069

Adj R-squared = 0.0474 Root MSE = .14619


open

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

open







L1.

.3173305

.148261

2.14

0.038

.0187175

.6159434

L2.

.0269943

.1557271

0.17

0.863

-.2866563

.3406448

L3.

-.0463866

.1506518

-0.31

0.760

-.3498149

.2570418

_cons

.605895

.1696411

3.57

0.001

.2642204

.9475696


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

49

24.00634

26.77727

4

-45.55455

-37.98727

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


Source

SS

df

MS

Model

.298067915

4

.074516979

Residual

.718903099

43

.016718677

Total

1.01697101

47

.021637681

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


Number of

obs =

48

F( 4,

43) =

4.46

Prob > F

=

0.0042

R-squared

=

0.2931

Adj R-squared = 0.2273 Root MSE = .1293


open

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

open







L1.

.3091412

.1320121

2.34

0.024

.0429134

.575369

L2.

.0042937

.1380847

0.03

0.975

-.2741806

.282768

L3.

-.1819545

.1383603

-1.32

0.195

-.4609846

.0970755

L4.

.4821225

.1382114

3.49

0.001

.2033927

.7608523

_cons

.3461166

.1752

1.98

0.055

-.0072079

.699441


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

48

24.39589

32.72046

5

-55.44093

-46.08492

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

.510412701

5

.10208254

Residual

.502960443

41

.012267328

Total

1.01337314

46

.022029851

Number of obs

=

47

F( 5, 41)

=

8.32

Prob > F

=

0.0000

R-squared

=

0.5037

Adj R-squared

=

0.4431

Root MSE

=

.11076


open

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

open







L1.

.57083

.1306305

4.37

0.000

.3070164

.8346436

L2.

-.0839843

.1203304

-0.70

0.489

-.3269965

.1590279

L3.

-.1702766

.1188024

-1.43

0.159

-.410203

.0696498

L4.

.6580299

.1256238

5.24

0.000

.4043274

.9117323

L5.

-.5633532

.1345012

-4.19

0.000

-.8349839

-.2917224

_cons

.5099221

.1602883

3.18

0.003

.1862133

.833631


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

47

23.47617

39.93859

6

-67.87717

-56.77629

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



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


Source

SS

df

MS

Model

.53264912

6

.088774853

Residual

.473580226

39

.012143083

Total

1.00622935

45

.022360652

Number of obs

=

46

F( 6, 39)

=

7.31

Prob > F

=

0.0000

R-squared

=

0.5294

Adj R-squared

=

0.4569

Root MSE

=

.1102


open

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

open







L1.

.6981034

.1554295

4.49

0.000

.3837175

1.012489

L2.

-.242282

.1573857

-1.54

0.132

-.5606247

.0760606

L3.

-.1356909

.1210945

-1.12

0.269

-.3806278

.1092459

L4.

.674898

.1261127

5.35

0.000

.419811

.9299851

L5.

-.7010408

.1621776

-4.32

0.000

-1.029076

-.3730056

L6.

.2353812

.1601469

1.47

0.150

-.0885465

.5593089

_cons

.4119859

.1823732

2.26

0.030

.0431012

.7808706


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

46

22.64475

39.97856

7

-65.95712

-53.15663

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

Source

SS

df

MS

Model

.526750427

7

.075250061

Residual

.46854242

37

.012663309

Total

.995292847

44

.022620292

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


Number of

obs =

45

F( 7,

37) =

5.94

Prob > F

=

0.0001

R-squared

=

0.5292

Adj R-squared = 0.4402 Root MSE = .11253


open

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

open







L1.

.6845875

.163539

4.19

0.000

.353226

1.015949

L2.

-.218067

.1969413

-1.11

0.275

-.617108

.1809739

L3.

-.1685896

.1656278

-1.02

0.315

-.5041834

.1670042

L4.

.6729599

.1320721

5.10

0.000

.4053565

.9405634

L5.

-.6951982

.169802

-4.09

0.000

-1.03925

-.3511468

L6.

.2087038

.2017781

1.03

0.308

-.2001374

.6175451

L7.

.0260837

.1709089

0.15

0.880

-.3202105

.372378

_cons

.4298774

.2055598

2.09

0.043

.0133736

.8463812


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

45

21.90383

38.85557

8

-61.71113

-47.25783

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) -2.208 -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.

-.4706311

.2131615

-2.21

0.033

-.901791

-.0394713

LD.

.1687345

.2019582

0.84

0.409

-.2397644

.5772335

L2D.

-.0735475

.2022638

-0.36

0.718

-.4826646

.3355696

L3D.

-.2092384

.1719986

-1.22

0.231

-.5571383

.1386615

L4D.

.4656596

.1506003

3.09

0.004

.1610418

.7702774

L5D.

-.2353812

.1601469

-1.47

0.150

-.5593089

.0885465

_cons

.4119859

.1823732

2.26

0.030

.0431012

.7808706


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

Phụ lục 2.5.4. KIỂM ĐỊNH TÍNH DỪNG CỦA BIẾN expv

Độ trễ tối ưu chọn theo tiêu chuẩn thông tin AIC nhỏ nhất là bậc 1 với AIC nhỏ nhất là -184.5335. 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 = 0.0058 < 1% nên giả thuyết H0 bị bác bỏ ở mức ý nghĩa 1% hay biến expv là chuỗi dừng tại bậc 0: I(0).


. varsoc expv, maxlag(8)


Selection-order criteria

Sample: 9 - 52 Number of obs = 44


lag

LL

LR

df

p

FPE

AIC

HQIC

SBIC

0

70.3354




.002505

-3.15161

-3.13657

-3.11106

1

81.1245

21.578*

1

0.000

.001605*

-3.59657*

-3.56649*

-3.51547*

2

82.0298

1.8106

1

0.178

.001613

-3.59226

-3.54715

-3.47061

3

82.4847

.90989

1

0.340

.001654

-3.56749

-3.50734

-3.40529

4

82.6546

.33978

1

0.560

.001718

-3.52975

-3.45457

-3.32701

5

82.6562

.00312

1

0.955

.001799

-3.48437

-3.39414

-3.24107

6

82.7645

.21667

1

0.642

.001875

-3.44384

-3.33858

-3.15999

7

84.1606

2.7923

1

0.095

.001844

-3.46185

-3.34154

-3.13745

8

84.9522

1.5832

1

0.208

.001865

-3.45237

-3.31703

-3.08743

Endogenous: expv Exogenous: _cons


.

. reg expv l.expv


Source

SS

df

MS

Model

.043424875

1

.043424875

Residual

.074066866

49

.001511569

Total

.117491741

50

.002349835

Number of obs =

51

F( 1, 49) =

28.73

Prob > F =

0.0000

R-squared =

0.3696

Adj R-squared =

0.3567

Root MSE =

.03888


expv

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

expv







L1.

.6109449

.1139847

5.36

0.000

.381884

.8400057

_cons

.0476814

.015307

3.12

0.003

.016921

.0784419


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

51

82.50106

94.26676

2

-184.5335

-180.6699

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


.


. reg expv l.expv l2.expv


Source

SS

df

MS

Model

.044392239

2

.02219612

Residual

.068384463

47

.001454989

Total

.112776702

49

.002301565


Number of

obs =

50

F( 2,

47) =

15.26

Prob > F

=

0.0000

R-squared

=

0.3936

Adj R-squared = 0.3678 Root MSE = .03814


expv

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

expv







L1.

.4901451

.1414326

3.47

0.001

.2056195

.7746707

L2.

.1822065

.141221

1.29

0.203

-.1018935

.4663065

_cons

.0387852

.0165395

2.35

0.023

.0055121

.0720583


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

50

81.41229

93.91889

3

-181.8378

-176.1017

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


.

Source

SS

df

MS

Model

.049530078

3

.016510026

Residual

.062306666

45

.001384593

Total

.111836744

48

.002329932

. reg expv l.expv l2.expv l3.expv


Number of

obs =

49

F( 3,

45) =

11.92

Prob > F

=

0.0000

R-squared

=

0.4429

Adj R-squared = 0.4057 Root MSE = .03721


expv

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

expv







L1.

.5690703

.143259

3.97

0.000

.2805318

.8576088

L2.

.2294759

.1554179

1.48

0.147

-.0835518

.5425036

L3.

-.1369371

.1406695

-0.97

0.336

-.42026

.1463858

_cons

.0417437

.0172035

2.43

0.019

.0070941

.0763934


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

49

79.49413

93.82594

4

-179.6519

-172.0846

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

.


. dfuller expv, 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) -2.627 -2.408 -1.678 -1.300

p-value for Z(t) = 0.0058



D.expv

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

expv







L1.

-.3276484

.1247212

-2.63

0.012

-.5785551

-.0767418

LD.

-.1822065

.141221

-1.29

0.203

-.4663065

.1018935

_cons

.0387852

.0165395

2.35

0.023

.0055121

.0720583


.


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

Phụ lục 2.5.5. KIỂM ĐỊNH TÍNH DỪNG CỦA BIẾN fpi

Độ trễ tối ưu chọn theo tiêu chuẩn thông tin AIC nhỏ nhất là bậc 5 với AIC nhỏ nhất là -135.8176. Kết quả kiểm định ADF ở bậc 5 cho dạng phương trình bước ngẫu nhiên có hệ số chặn (random walk with drift) có p-value = 0.0008 < 1% nên giả thuyết H0 bị bác bỏ ở mức ý nghĩa 1% hay biến fpi là chuỗi dừng tại bậc 0: I(0).



. varsoc fpi, maxlag(8)


Selection-order criteria

Sample: 9 - 52 Number of obs = 44


lag

LL

LR

df

p

FPE

AIC

HQIC

SBIC

0

47.7152




.007004

-2.12342

-2.10838

-2.08287

1

48.924

2.4176

1

0.120

.006938

-2.13291

-2.10283

-2.05181

2

49.7887

1.7294

1

0.188

.006982

-2.12676

-2.08164

-2.00511

3

50.3527

1.128

1

0.288

.007124

-2.10694

-2.04679

-1.94474

4

56.1105

11.516

1

0.001

.005741

-2.3232

-2.24802

-2.12046

5

72.8259

33.431*

1

0.000

.002812*

-3.03754*

-2.94731*

-2.79424*

6

72.8661

.08038

1

0.777

.002941

-2.99391

-2.88865

-2.71006

7

74.099

2.4658

1

0.116

.002914

-3.0045

-2.8842

-2.6801

8

74.6816

1.1653

1

0.280

.002975

-2.98553

-2.85019

-2.62058

Endogenous: fpi Exogenous: _cons

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