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Ước lượng mức dự trữ ngoại hối tối ưu của Việt Nam - 30

Source

SS

df

MS

Model

9.79065216

1

9.79065216

Residual

.623666039

49

.012727878

Total

10.4143182

50

.208286364

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Ước lượng mức dự trữ ngoại hối tối ưu của Việt Nam - 30

. reg lnres l.lnres


Number of

obs =

51

F( 1,

49) =

769.23

Prob > F

=

0.0000

R-squared

=

0.9401

Adj R-squared = 0.9389 Root MSE = .11282


lnres

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

lnres







L1.

.956818

.0344986

27.73

0.000

.8874905

1.026146

_cons

1.062323

.820439

1.29

0.201

-.5864107

2.711057


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

51

-31.85544

39.93526

2

-75.87053

-72.00688

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



Source

SS

df

MS

Model

8.79945486

2

4.39972743

Residual

.576502723

47

.012266015

Total

9.37595758

49

.191346073

. reg lnres l.lnres l2.lnres



Number of

obs =

50

F( 2,

47) =

358.69

Prob > F

=

0.0000

R-squared

=

0.9385

Adj R-squared = 0.9359 Root MSE = .11075


lnres

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

lnres







L1.

1.196929

.14353

8.34

0.000

.9081839

1.485674

L2.

-.2504738

.1400998

-1.79

0.080

-.5323182

.0313707

_cons

1.302368

.8447824

1.54

0.130

-.3971151

3.001851


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

50

-29.10007

40.62303

3

-75.24606

-69.50999

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

. reg lnres l.lnres l2.lnres l3.lnres


Source

SS

df

MS

Model

7.89938477

3

2.63312826

Residual

.574670465

45

.012770455

Total

8.47405524

48

.176542817

Number of obs = 49

F( 3, 45) = 206.19

Prob > F = 0.0000

R-squared = 0.9322 Adj R-squared = 0.9277 Root MSE = .11301


lnres

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

lnres







L1.

1.212849

.1526242

7.95

0.000

.9054477

1.52025

L2.

-.3184872

.2324625

-1.37

0.177

-.7866908

.1497164

L3.

.054428

.1478291

0.37

0.714

-.2433151

.3521712

_cons

1.247892

.9140163

1.37

0.179

-.5930315

3.088815


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

49

-26.53512

39.39359

4

-70.78719

-63.2199

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


. dfuller lnres, 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) -1.508 -2.408 -1.678 -1.300

p-value for Z(t) = 0.0691


D.lnres

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

lnres







L1.

-.0535447

.0354989

-1.51

0.138

-.1249593

.0178699

LD.

.2504738

.1400998

1.79

0.080

-.0313707

.5323182

_cons

1.302368

.8447824

1.54

0.130

-.3971151

3.001851


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

Phụ lục 2.5.2. KIỂM ĐỊNH TÍNH DỪNG CỦA BIẾN lngdp

Độ trễ tối ưu chọn theo tiêu chuẩn thông tin AIC nhỏ nhất là bậc 4 với AIC nhỏ nhất là -89.0596. Kết quả kiểm định ADF ở bậc 4 cho dạng phương trình bước ngẫu nhiên có hệ số chặn (random walk with drift) có p-value = 0.0577 < 10% nên giả thuyết H0 bị bác bỏ ở mức ý nghĩa 10% hay biến lngdp là chuỗi dừng tại bậc 0: I(0).



. varsoc lngdp, maxlag(8)


Selection-order criteria

Sample: 9 - 52 Number of obs = 44


lag

LL

LR

df

p

FPE

AIC

HQIC

SBIC

0

-24.4176




.185904

1.15535

1.17038

1.19589

1

-3.79146

41.252

1

0.000

.076187

.263248

.293324

.344348

2

3.17087

13.925

1

0.000

.058109

-.007767

.037347

.113883

3

4.32056

2.2994

1

0.129

.057732

-.014571

.04558

.147628

4

44.0702

79.499*

1

0.000

.009924

-1.77592

-1.70073*

-1.57317*

5

45.3181

2.4958

1

0.114

.00982*

-1.78719*

-1.69696

-1.54389

6

45.7302

.82423

1

0.364

.010096

-1.76046

-1.6552

-1.47662

7

46.7223

1.9841

1

0.159

.010113

-1.7601

-1.6398

-1.43571

8

47.833

2.2214

1

0.136

.01008

-1.76514

-1.6298

-1.40019

Endogenous: lngdp Exogenous: _cons

Source

SS

df

MS

Model

8.64168935

1

8.64168935

Residual

3.34939799

49

.068355061

Total

11.9910873

50

.239821747

. reg lngdp l.lngdp


Number of

obs =

51

F( 1,

49) =

126.42

Prob > F

=

0.0000

R-squared

=

0.7207

Adj R-squared = 0.7150 Root MSE = .26145


lngdp

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

lngdp







L1.

.8429359

.0749688

11.24

0.000

.6922804

.9935913

_cons

3.818746

1.804747

2.12

0.039

.1919712

7.44552


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

51

-35.45048

-2.928217

2

9.856435

13.72009

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


Source

SS

df

MS

Model

9.04050178

2

4.52025089

Residual

2.34138377

47

.049816676

Total

11.3818855

49

.232283379

. reg lngdp l.lngdp l2.lngdp


Number of

obs =

50

F( 2,

47) =

90.74

Prob > F

=

0.0000

R-squared

=

0.7943

Adj R-squared = 0.7855 Root MSE = .2232


lngdp

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

lngdp







L1.

.3798737

.1243266

3.06

0.004

.1297607

.6299866

L2.

.5440194

.1213136

4.48

0.000

.2999679

.7880709

_cons

1.885452

1.659215

1.14

0.262

-1.452458

5.223363


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

50

-33.94693

5.585096

3

-5.170191

.5658775

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

. reg lngdp l.lngdp l2.lngdp l3.lngdp


Source

SS

df

MS

Model

8.4249582

3

2.8083194

Residual

2.2398737

45

.049774971

Total

10.6648319

48

.222183998

Number of obs = 49

F( 3, 45) = 56.42

Prob > F = 0.0000

R-squared = 0.7900 Adj R-squared = 0.7760 Root MSE = .2231


lngdp

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

lngdp







L1.

.2609

.1498213

1.74

0.088

-.0408555

.5626555

L2.

.4613535

.1360609

3.39

0.001

.1873127

.7353943

L3.

.2084649

.1460955

1.43

0.161

-.0857865

.5027164

_cons

1.731463

1.727599

1.00

0.322

-1.7481

5.211026


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

49

-32.1687

6.064332

4

-4.128664

3.438617

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


. reg lngdp l.lngdp l2.lngdp l3.lngdp l4.lngdp


Source

SS

df

MS

Model

9.86815305

4

2.46703826

Residual

.356857872

43

.00829902

Total

10.2250109

47

.217553424

Number of obs = 48

F( 4, 43) = 297.27

Prob > F = 0.0000

R-squared = 0.9651 Adj R-squared = 0.9619 Root MSE = .0911


lngdp

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

lngdp







L1.

.0281927

.0631336

0.45

0.657

-.0991282

.1555137

L2.

-.0014267

.0634879

-0.02

0.982

-.1294622

.1266088

L3.

-.0180451

.0623963

-0.29

0.774

-.1438792

.1077891

L4.

.9321682

.0620829

15.01

0.000

.806966

1.05737

_cons

1.537002

.7322773

2.10

0.042

.0602241

3.01378


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

48

-30.99631

49.5298

5

-89.0596

-79.70359

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

. reg lngdp l.lngdp l2.lngdp l3.lngdp l4.lngdp l5.lngdp

Source

SS

df

MS

Model

8.91084585

5

1.78216917

Residual

.331204995

41

.008078171

Total

9.24205084

46

.200914149

Number of obs = 47

F( 5, 41) = 220.62

Prob > F = 0.0000

R-squared = 0.9642 Adj R-squared = 0.9598 Root MSE = .08988


lngdp

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

lngdp







L1.

.2518645

.1532909

1.64

0.108

-.0577128

.5614417

L2.

.0012951

.0627332

0.02

0.984

-.1253971

.1279873

L3.

.0006874

.0627133

0.01

0.991

-.1259646

.1273394

L4.

.9357866

.0625528

14.96

0.000

.8094587

1.062115

L5.

-.2413711

.1536911

-1.57

0.124

-.5517565

.0690143

_cons

1.333781

.780247

1.71

0.095

-.24196

2.909521


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

47

-28.47009

49.75628

6

-87.51255

-76.41167

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


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


Source

SS

df

MS

Model

8.36459426

6

1.39409904

Residual

.322576248

39

.008271186

Total

8.68717051

45

.193048233

Number of obs = 46

F( 6, 39) = 168.55

Prob > F = 0.0000

R-squared = 0.9629 Adj R-squared = 0.9572 Root MSE = .09095


lngdp

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

lngdp







L1.

.2050911

.1617911

1.27

0.212

-.1221623

.5323445

L2.

.1286753

.1602828

0.80

0.427

-.1955272

.4528778

L3.

.0022471

.0635597

0.04

0.972

-.1263145

.1308088

L4.

.9468591

.0645727

14.66

0.000

.8162484

1.07747

L5.

-.1987316

.1619399

-1.23

0.227

-.5262859

.1288227

L6.

-.136251

.1607205

-0.85

0.402

-.4613389

.1888369

_cons

1.333503

.8385973

1.59

0.120

-.3627198

3.029727


. estat ic


Akaike's information criterion and Bayesian information criterion


Model

Obs

ll(null)

ll(model)

df

AIC

BIC

.

46

-26.93491

48.81014

7

-83.62028

-70.81979

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

. dfuller lngdp, lags(4) drift reg


Augmented Dickey-Fuller test for unit root Number of obs = 47

Z(t) has t-distribution

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

Z(t) -1.609 -2.421 -1.683 -1.303

p-value for Z(t) = 0.0577



D.lngdp

Coef.

Std. Err.

t

P>|t|

[95% Conf.

Interval]

lngdp







L1.

-.0517375

.0321636

-1.61

0.115

-.1166931

.0132182

LD.

-.6963981

.1463356

-4.76

0.000

-.9919289

-.4008673

L2D.

-.6951029

.1539203

-4.52

0.000

-1.005951

-.3842546

L3D.

-.6944155

.1586306

-4.38

0.000

-1.014777

-.3740545

L4D.

.2413711

.1536911

1.57

0.124

-.0690143

.5517565

_cons

1.333781

.780247

1.71

0.095

-.24196

2.909521


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

Phụ lục 2.5.3. 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 -67.87717. 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

22.1714




.022366

-.962338

-.9473

-.921788

1

23.8408

3.3388

1

0.068

.021697

-.992764

-.962689

-.911665

2

23.8678

.05394

1

0.816

.022682

-.948536

-.903422

-.826886

3

24.0447

.35381

1

0.552

.023553

-.911122

-.850971

-.748923

4

28.582

9.0745

1

0.003

.020065

-1.07191

-.996718

-.869158

5

37.1297

17.095*

1

0.000

.014248*

-1.41499*

-1.32476*

-1.17169*

6

38.0718

1.8842

1

0.170

.0143

-1.41235

-1.30709

-1.12851

7

38.0756

.00755

1

0.931

.014983

-1.36707

-1.24677

-1.04267

8

38.2115

.27191

1

0.602

.01561

-1.3278

-1.19246

-.962848

Endogenous: open Exogenous: _cons

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