## Adf And Kpss Tests Economics Essay

Before showing the consequences, it is importance to look into for the clip series informations being of stationary belongings of each variable whether the variables under consideration are stationary in flat signifier. It is of import to guarantee that the variables used in the arrested development are non capable to specious correlativity. We are utilizing unit root trials to look into the stationary position of each variable by Augmented Dickey-Fuller ( ADF ) and Kwiatkowski, Phillips, Schmidt and Shin ( KPSS ) . These two trials will prove for with and without clip tendency at flat signifier and indicate slowdown lengths based on the Akaike Information Criterion ( AIC ) . Table 1 is the consequence for ADF while Table 2 is KPSS.

Table 1 show that CPI and RGFCF, both are important at 5 % degree at the degree signifier without additive tendency ; important at 1 % degree for the flat signifier with additive tendency. RGDP is important at 10 % degree for the degree signifier without tendency and important at 5 % degree with additive tendency. While for the POP, it can non important at flat signifier without additive tendency. However, POP important at 5 % degree for the flat signifier with additive tendency.

Table 2 shows that all variables are important at 1 % degree for both with and without additive tendency at flat signifier. Which is explained that the t-statistic have sufficient grounds do non reject the void hypothesis at flat signifier in both with and without additive tendency. These mean that the series are stationary at flat signifier.

In short, all the variables show stationary at the degree signifier, I ( 0 ) . Which it obeys the theory or construct of stationarity of fiscal informations where it is predicted to be non-stationary at flat signifier and stationary after the first difference. Thus this allows us to continue to the cointegration trials.

## Variables

## Augmented Dickey-Fuller

## degree

## Changeless without additive tendency

## Changeless with additive tendency

## Consumer price index

## -3.042517 ** ( 3 )

## -6.276123 *** ( 7 )

## RGFCF

## -3.393829 ** ( 1 )

## -12.31312 *** ( 1 )

## RGDP

## -2.632101 * ( 4 )

## -4.034734 ** ( 3 )

## Dad

## -0.659324 ( 1 )

## -4.187427 ** ( 6 )

## Table 1: Consequences of Unit Root tests with ADF

Notes: figures within parentheses indicate lag lengths. Lag length for ADF trials have been decided on the footing of Akaike Information Criterion ( AIC ) ( Akaike, 1974 ) . The ADF trials are based on the void hypothesis of unit roots. *** , ** , and * indicate important at 1 % , 5 % and 10 % degrees severally, based on the critical T statistics as computed by Mackinnon ( 1996 ) .

## Table 2: Consequences of Unit Roots tests with KPSS

Variables

Kwiatkowski, Phillips, Schmidt and Shin ( KPSS )

degree

Changeless without additive tendency

Changeless with additive tendency

Consumer price index

0.170339 ***

0.172165 ***

RGFCF

0.686537 ***

0.086532 ***

RGDP

0.120369 ***

0.065365 ***

Dad

0.491314 ***

0.117854 ***

Notes: figures within parentheses indicate lag lengths. Lag length for ADF trials have been decided on the footing of Kwiatkowski, Phillips, Schmidt and Shin ( KPSS ) . The KPSS trials are based on the void hypothesis of unit roots. *** , ** , and * indicate important at 1 % , 5 % and 10 % degrees severally, based on the critical T statistics as computed by Mackinnon ( 1996 ) .

## 4.2 Granger Causality Trials

After identify ADP and KPSS trial to guarantee all the variables are stationary, we used Granger Causality trial to gauge the additive causing between rising prices and economic growing and the consequences are shown in Table 3.

Table 3: Pair wise Granger Causality Trials

Sample: 1960 – 2005

Slowdowns: 1

Null Hypothesis:

Ob river

F-Statistic

Prob.

Consumer price index does non Granger Cause RGDP

44

4.68436

0.0363

RGDP does non Granger Cause CPI

21.2996

4.E-05

Both the void hypothesis is rejected at 1-5 per centum degree of significance, which implies that rising prices rate does Granger Causality existent GDP growing and existent GDP growing does Granger Causality rising prices rate. This trial statistic shows that the causality between two variables is bi-directed. The variables are co-integrated because there is a long-term relationship between rising prices rate and existent GDP growing. We farther applied the Akaike Information Criterion ( AIC ) and Schwarz Information Criterion ( SIC ) to specify the slowdown length used for rising prices rate and existent GDP growing. The consequence is of import to place the pick of dependant and independent variable for the threshold theoretical account specification. In add-on, the rising prices rate is doing growing at slowdown one ( lag=1 ) for the period from 1961 to 2005. Hence, we generate the equation by adding slowdown one for the rising prices rate in the appraisal theoretical account.

## 4.3 Threshold Model appraisal

We run the information utilizing ordinary least squares ( OLS ) econometric technique and the rising prices rate are kept at slowdown one after gauging for Granger Causality trial. The optimum threshold degree is the minimal value of RSS ( residuary amount of squares ) as shown in table 4. Table 4 besides illustrates the consequence of t-statistics and P-values for the appraisal equation. From the consequence, it shows that both 3 and 5 per centum rising prices degree besides get the minimal value of RSS. This means that the optimum degree of threshold is approximately 3 to 5 per centum. In order to happen out the threshold degree of rising prices, we farther specified the value of K from the scope of 3 to 5 per centum into per centum point and the appraisal value is shown as Table 5. The consequences in Table 5 indicated that 3.1 per centum rising prices degree is the optimum degree of threshold in which the value of K is the 1 with minimizes the residuary amount of squares ( RSS ) .

4.3.1 Estimation of OLS arrested development

Table 4: Appraisal of OLS arrested development at K = 1 to 5 %

( Dependent Variable: existent GDP growing )

K ( % )

Variable

Coefficient

Std. Mistake

t-statistics

P- value

Roentgen

1

Inflation

Inflation ( -1 )

( inf & A ; gt ; 1 ) * ( inf-1 )

Investing growing

Population growing

C

1.538332

-0.564809

-0.976010

0.170379

-1.939174

9.314812

0.583956

0.298513

0.663202

0.035571

0.444884

1.162268

2.634331

-1.892076

-1.471665

4.789804

-4.358832

8.014344

0.0121

0.0661

0.0000

0.0001

0.1493

0.0000

7.223265

2

Inflation

Inflation ( -1 )

( inf & A ; gt ; 2 ) * ( inf-2 )

Investing growing

Population growing

C

0.969290

-0.582696

-0.420789

0.175398

-1.982495

9.648625

0.336110

0.302481

0.337848

0.040910

0.459113

1.116383

2.883850

-1.926386

-1.245497

4.287397

-4.318101

8.642757

0.0064

0.0616

0.2206

0.0001

0.0001

0.0000

7.335498

3

Inflation

Inflation ( -1 )

( inf & A ; gt ; 3 ) * ( inf-3 )

Investing growing

Population growing

C

0.826579

-0.599289

-0.359792

0.188432

-2.344833

10.63177

0.304288

0.283725

0.202986

0.039605

0.522635

1.144140

2.716439

-2.112214

-1.772499

4.757815

-4.486557

9.292370

0.0099

0.0413

0.0843

0.0000

0.0001

0.0000

7.051917

4

Inflation

Inflation ( -1 )

( inf & A ; gt ; 4 ) * ( inf-4 )

Investing growing

Population growing

C

0.811575

-0.749978

0.113362

0.128502

-1.700261

9.726264

0.315729

0.291090

0.257392

0.038080

0.532298

1.201991

2.570482

-2.576447

0.440428

3.374569

-3.194193

8.091795

0.0142

0.0140

0.6621

0.0017

0.0028

0.0000

7.596177

5

Inflation

Inflation ( -1 )

( inf & A ; gt ; 5 ) * ( inf-5 )

Investing growing

Population growing

C

0.826574

-0.789422

0.821631

0.110625

-1.472938

9.327557

0.304094

0.277041

0.459772

0.032438

0.475637

1.123008

2.718158

-2.849476

1.787040

3.410344

-3.096766

8.305869

0.0098

0.0070

0.0819

0.0016

0.0037

0.0000

7.043056

* ( inf & A ; gt ; K ) * ( inf-k ) denotes the silent person variable

Table 5: Appraisal of OLS arrested development at K = 3 to 5 %

( Dependent Variable: existent GDP growing )

K ( % )

Variable

Coefficient

Std. Mistake

t-statistics

P- value

Roentgen

3

Inflation

Inflation ( -1 )

( inf & A ; gt ; 3 ) * ( inf-3 )

Investing growing

Population growing

C

0.826579

-0.599289

-0.359792

0.188432

-2.344833

10.63177

0.304288

0.283725

0.202986

0.039605

0.522635

1.144140

2.716439

-2.112214

-1.772499

4.757815

-4.486557

9.292370

0.0099

0.0413

0.0843

0.0000

0.0001

0.0000

7.051917

3.1

Inflation

Inflation ( -1 )

( inf & A ; gt ; 3.1 ) * ( inf-3.1 )

Investing growing

Population growing

C

0.819686

-0.599990

-0.365668

0.189027

-2.365807

10.68969

0.303305

0.282258

0.198960

0.039084

0.522171

1.147435

2.702515

-2.125684

-1.837890

4.836480

-4.530714

9.316161

0.0102

0.0401

0.0739

0.0000

0.0001

0.0000

7.011681

3.2

Inflation

Inflation ( -1 )

( inf & A ; gt ; 3.2 ) * ( inf-3.2 )

Investing growing

Population growing

C

0.813290

-0.609849

-0.346033

0.185869

-2.341698

10.67837

0.304849

0.283432

0.201610

0.039245

0.527866

1.161110

2.667843

-2.151662

-1.716349

4.736069

-4.436160

9.196697

0.0112

0.0378

0.0942

0.0000

0.0001

0.0000

7.085655

3.3

Inflation

Inflation ( -1 )

( inf & A ; gt ; 3.3 ) * ( inf-3.3 )

Investing growing

Population growing

C

0.808974

-0.630219

-0.292763

0.177525

-2.253800

10.55958

0.308498

0.286697

0.208103

0.039526

0.534224

1.177629

2.622301

-2.198207

-1.406819

4.491332

-4.218832

8.966817

0.0125

0.0341

0.1676

0.0001

0.0001

0.0000

7.256989

3.4

Inflation

Inflation ( -1 )

( inf & A ; gt ; 3.4 ) * ( inf-3.4 )

Investing growing

Population growing

C

0.805122

-0.650546

-0.232901

0.168643

-2.156660

10.42119

0.311662

0.289750

0.214940

0.039689

0.538534

1.190453

2.583316

-2.245200

-1.083561

4.249078

-4.004684

8.753971

0.0138

0.0307

0.2854

0.0001

0.0003

0.0000

7.406122

3.6

Inflation

Inflation ( -1 )

( inf & A ; gt ; 3.6 ) * ( inf-3.6 )

Investing growing

Population growing

C

0.807381

-0.691159

-0.122278

0.153417

-1.988913

10.17585

0.315266

0.291690

0.230561

0.039680

0.543951

1.207686

2.560950

-2.369499

-0.530351

3.866320

-3.656419

8.425900

0.0145

0.0230

0.5990

0.0004

0.0008

0.0000

7.578855

3.7

Inflation

Inflation ( -1 )

( inf & A ; gt ; 3.7 ) * ( inf-3.7 )

Investing growing

Population growing

C

0.807354

-0.706396

-0.069755

0.146940

-1.915185

10.06400

0.316081

0.292153

0.238509

0.039497

0.543696

1.210660

2.554263

-2.417897

-0.292465

3.720337

-3.522527

8.312827

0.0148

0.0205

0.7715

0.0006

0.0011

0.0000

7.617805

3.8

Inflation

Inflation ( -1 )

( inf & A ; gt ; 3.8 ) * ( inf-3.8 )

Investing growing

Population growing

C

0.807750

-0.720644

-0.015039

0.140795

-1.844324

9.954601

0.316440

0.292325

0.246159

0.039191

0.541739

1.210691

2.552617

-2.465216

-0.061095

3.592527

-3.404451

8.222247

0.0148

0.0183

0.9516

0.0009

0.0016

0.0000

7.634203

3.9

Inflation

Inflation ( -1 )

( inf & A ; gt ; 3.9 ) * ( inf-3.9 )

Investing growing

Population growing

C

0.809120

-0.735339

0.046430

0.134571

-1.771822

9.840773

0.316347

0.291994

0.252579

0.038722

0.537991

1.207920

2.557700

-2.518335

0.183822

3.475323

-3.293408

8.146872

0.0146

0.0161

0.8551

0.0013

0.0021

0.0000

7.628169

4

Inflation

Inflation ( -1 )

( inf & A ; gt ; 4 ) * ( inf-4 )

Investing growing

Population growing

C

0.811575

-0.749978

0.113362

0.128502

-1.700261

9.726264

0.315729

0.291090

0.257392

0.038080

0.532298

1.201991

2.570482

-2.576447

0.440428

3.374569

-3.194193

8.091795

0.0142

0.0140

0.6621

0.0017

0.0028

0.0000

7.596177

4.1

Inflation

Inflation ( -1 )

( inf & A ; gt ; 4.1 ) * ( inf-4.1 )

Investing growing

Population growing

C

0.813492

-0.760287

0.170993

0.124043

-1.646754

9.638877

0.314833

0.289823

0.265606

0.037537

0.527469

1.196228

2.583886

-2.623281

0.643784

3.304522

3.121994

8.057725

0.0137

0.0125

0.5236

0.0021

0.0034

0.0000

7.552578

4.2

Inflation

Inflation ( -1 )

( inf & A ; gt ; 4.2 ) * ( inf-4.2 )

Investing growing

Population growing

C

0.814782

-0.767124

0.220848

0.120950

-1.609142

9.576530

0.313960

0.288694

0.278565

0.037145

0.523851

1.191449

2.595175

-2.657221

0.792806

3.256148

-3.071754

8.037718

0.0134

0.0115

0.4328

0.0024

0.0039

0.0000

7.510721

4.3

Inflation

Inflation ( -1 )

( inf & A ; gt ; 4.3 ) * ( inf-4.3 )

Investing growing

Population growing

C

0.816150

-0.773634

0.278128

0.117888

-1.571341

9.512792

0.312816

0.287248

0.291258

0.036629

0.518916

1.184728

2.609045

-2.693259

0.954918

3.218462

-3.028123

8.029515

0.0129

0.0105

0.3457

0.0026

0.0044

0.0000

7.456034

4.4

Inflation

Inflation ( -1 )

( inf & A ; gt ; 4.4 ) * ( inf-4.4 )

Investing growing

Population growing

C

0.817551

-0.779375

0.342362

0.115024

-1.535224

9.450467

0.311364

0.285454

0.303176

0.035972

0.512475

1.175766

2.625705

-2.730304

1.129254

3.197581

-2.995704

8.037712

0.0124

0.0095

0.2659

0.0028

0.0048

0.0000

7.387056

4.5

Inflation

Inflation ( -1 )

( inf & A ; gt ; 4.5 ) * ( inf-4.5 )

Investing growing

Population growing

C

0.819140

-0.783980

0.411602

0.112607

-1.503814

9.394421

0.309627

0.283369

0.314048

0.035185

0.504553

1.164552

2.645568

-2.766638

1.310632

3.200417

-2.980485

8.066984

0.0118

0.0087

0.1978

0.0028

0.0050

0.0000

7.304748

4.6

Inflation

Inflation ( -1 )

( inf & A ; gt ; 4.6 ) * ( inf-4.6 )

Investing growing

Population growing

C

0.826182

-0.791957

0.475658

0.111246

-1.488082

9.365990

0.308440

0.282444

0.331298

0.034615

0.497872

1.154816

2.678578

-2.803944

1.435742

3.213786

-2.988884

8.110371

0.0109

0.0079

0.1593

0.0027

0.0049

0.0000

7.242096

4.7

Inflation

Inflation ( -1 )

( inf & A ; gt ; 4.7 ) * ( inf-4.7 )

Investing growing

Population growing

C

0.831194

-0.797041

0.545831

0.110370

-1.477221

9.344919

0.307201

0.281199

0.351402

0.034007

0.491151

1.145051

2.705703

-2.834436

1.553297

3.245510

-3.007671

8.161138

0.0101

0.0073

0.1286

0.0024

0.0047

0.0000

7.179129

4.8

Inflation

Inflation ( -1 )

( inf & A ; gt ; 4.8 ) * ( inf-4.8 )

Investing growing

Population growing

C

0.827864

-0.793620

0.626005

0.110009

-1.469475

9.328254

0.305943

0.279437

0.380078

0.033468

0.486068

1.137663

2.705945

-2.840064

1.647046

3.286991

-3.023188

8.199489

0.0101

0.0072

0.1078

0.0022

0.0045

0.0000

7.126222

4.9

Inflation

Inflation ( -1 )

( inf & A ; gt ; 4.9 ) * ( inf-4.9 )

Investing growing

Population growing

C

0.823792

-0.788270

0.715461

0.110231

-1.468281

9.321785

0.304688

0.277659

0.411601

0.032828

0.479930

1.129002

2.703724

-2.838987

1.738240

3.357893

-3.059364

8.256657

0.0102

0.0072

0.0903

0.0018

0.0041

0.0000

7.072592

5

Inflation

Inflation ( -1 )

( inf & A ; gt ; 5 ) * ( inf-5 )

Investing growing

Population growing

C

0.826574

-0.789422

0.821631

0.110625

-1.472938

9.327557

0.304094

0.277041

0.459772

0.032438

0.475637

1.123008

2.718158

-2.849476

1.787040

3.410344

-3.096766

8.305869

0.0098

0.0070

0.0819

0.0016

0.0037

0.0000

7.043056

* ( inf & A ; gt ; K ) * ( inf-k ) denotes the silent person variable

The appraisal outputs for the theoretical account are generated as shown below:

GROWTH t = 10.6897 + 0.8197INF t – 0.56INF T -1- 0.3657DUMMY

– 2.3658POP T + 0.189INV T

t-stat = ( 9.3162 ) ( 2.7025 ) ( -2.1257 ) ( -1.8379 )

( -4.5307 ) ( 4.8365 )

R-squared = 0.7391

F-stats = 21.5272 ; Prob ( F-statistic ) = 0.0000

S.E. of arrested development = 0.4296 ; Mean dependant variable = 6.7262

Ceteris paribus, peers to 0.8197 agencies that when rising prices rate rises by 1 per centum, existent GDP growing rate will increase by 0.82 per centum on norm. is 0.56, which indicates that when old twelvemonth rising prices rate rises by 1 per centum, the existent GDP growing rate will diminish by 0.56 per centum on norm. is – 0.3657, indicates that when the threshold degree of rising prices additions by 1 per centum, the existent GDP growing rate will diminish by 0.37 per centum on norm. is -2.3658, bespeaking that when population growing additions by 1 per centum, the existent GDP growing rate will diminish by 2.37 per centum on norm. is 0.189 agencies that for every 1 per centum addition in existent investing, the existent GDP growing rate will be increased by 0.19 per centum on norm.

## 4.3.2 Test of significance

Table 6

T-test:

## Coefficient

## ?1 ( inf )

## ?2 ( inf ( — 1 ) )

## ?3 silent person

## ?4 pop

## ?5 inv

## Exp. Sign

positive

negative

negative

negative

positive

## t-stat

2.7025

-2.1257

-1.8379

-4.5307

4.8365

## P-value

0.0102**

0.0401**

0.0739*

0.0001***

0.0000***

## Decision

Significant

Significant

Significant

Significant

Significant

The T- trials are based on the void hypothesis. *** , ** , and * indicate important at 1 % , 5 % and 10 % degrees severally, based on the critical T statistics as computed by Mackinnon ( 1996 ) .

H0: ?i = 0

H1: ?i ? 0, I = 1, 2, 3, 4, 5

Decision regulation: Reject H0 if P-value is smaller than ? = 0.1, otherwise do non reject H0.

Decision: There is sufficient grounds to reason that all variables are significantly impacting existent GDP at ? = 0.1.

F-test:

H0: ?1 = ?2 = ?3 = ?4= ?5 = 0 ;

H1: At least one ? is ? 0

Decision regulation: Reject H0 if Prob ( F-stat ) smaller than ? = 0.1, otherwise do non reject H0

Decision: Since Prob ( F-stat ) is 0.0000 which is smaller than 0.1, therefore we reject H0

Decision: There is sufficient grounds to turn out that there is at least one of the explanatory variables is significantly impacting the economic growing in Malaysia.

Goodness of tantrum:

= 0.7391, which indicates that there is 73.91 per centum of the fluctuation in existent GDP of Malaysia that can be explained by the fluctuations of rising prices rate, old twelvemonth rising prices rate, threshold degree of rising prices, population growing and existent investing.

Standard error-to-mean ratio:

Standard error-to-mean ratio = ( S.E. of arrested development / Mean dependant variable ) *100 %

= ( 0.4296/ 6.7262 ) *100 %

= 6.39 %

The standard mistake of arrested development is 6.39 % , which is considered really little, indicates

that the estimations value is near to the true value. Hence, this theoretical account is a good tantrum.

## 4.4 Diagnostic Checking

We show the optimum degree of rising prices for the diagnostic checking and it illustrated in Table 6. For diagnostic testing, the job such as multicolinearity, autocorrelation, heteroskedasticity, theoretical account misspecification mistake and normalcy trial for residuary are included. The consequence is shown as below:

Table 6: Diagnostic trials for equation at k=3.1 %

Trial

Hypothesis

Statisticss

Consequence

Multicollinearity

VIF= 1/ ( 1-R2 ) = 1/ ( 1-0.4838 ) =1.9372

Not serious multi. job

Autocorrelation

H0: There is no autocorrelation job

Prob.Chi-Square ( 2 ) = 0.0000

cull H0

H1: There is autocorrelation job

Heteroskedasti

-city

H0: There is no heteroskedasticity job

Prob. F ( 1,41 ) = 0.0000

cull H0

H1: There is heteroskedasticity job

Misspecification trial

H0: There is no misspecification mistake

Prob. F ( 1,37 ) = 0.7274

Do non reject H0

H1: There is misspecification mistake

Note: ** denote important degree at 5percent

Normally distribution:

Figure 1:

For normality trial for residuary, Figure 1 shows that the P-value of JB-stat trial is 0.423364, which is higher than 5 % important degree. Therefore we do non reject void hypothesis at 5 % important degree and conclude that the residuary are usually distributed.

For multicollinearity trial, we perform regression analysis for the extremely correlative brace of independent variables to acquire the R squared and cipher the VIF. Table 7 is the correlativity analysis for every brace of independent variables.

Table 7: Correlation checking

RGDP

Consumer price index

RGFCF

Dad

_RGDP

1

0.529119

0.517047

-0.07067

Consumer price index

0.529119

1

0.108155

-0.39804

RGFCF

0.517047

0.108155

1

## 0.695556

_POP

-0.07067

-0.39804

0.695556

1

From the tabular array above, the consequence shows that population and existent investing are extremely correlated which is about 0.695556.

POP = 2.1984+0.04063RGFCF

R-squared = 0.4838

VIF ( Variance Inflation Factor ) = 1.9372

VIF is 1.9372 which is less than 10, this claim that there is non a serious multicollinearity job between Population and existent investing. Hence, we can go forth the theoretical account entirely if the VIF is non so serious and t-stat is statistically important.

We use Breusch-Godfrey Serial Correlation LM Test to analyze the being of autocorrelation. From the consequence showed above, the P-value of the Chi-square trial is 0.0000 which is smaller than 5 % important degree. Therefore we reject void hypothesis at 5 % important degree since there is sufficient grounds to reason that there is first order autocorrelation job in the theoretical account.

The motivation of running the ARCH Test is to analyze the being of heteroscedasticity. From the consequence showed above, the P-value of F-stat trial is 0.0000 which is smaller than 5 % important degree. Hence, we reject void hypothesis at 5 % important degree since there is sufficient grounds to reason that it is heteroskedasticity job in the theoretical account.

The intent for Ramsey Reset Test is to prove the misspecification mistake in the theoretical account. Based on the end product above, the P-value of F-stat trial is 0.7274 and it is more than 5 % important degree. Therefore, we do non reject void hypothesis at 5 % important degree since there is deficient grounds to turn out that there is misspecification mistake in the theoretical account.

The consequences above indicate that there is an autocorrelation and heteroskedasticity job take topographic point in the estimated equation. Therefore, we applied White ‘s process to work out this job and the consequence is shows in Table 8. We comparing the end product with regular OLS end product to look into whether heteroskedasticity is a serious job in the theoretical account. The White ‘s Heteroscedasticity-Consistent Variance and standard mistakes, besides known as robust standard mistakes can be implemented so as to asymptotically valid statistical inference can be made about the true parametric quantity values ( Gujarti ) . Furthermore, we examined White ‘s Heteroscedasticity-Consistent Variance and standard mistakes along with the OLS discrepancies and standard mistakes.

Table 8: White Test at k=3.1 %

K ( % )

Variable

Coefficient

Std. Mistake

t-statistics

P- value

Roentgen

3.1

Inflation

Inflation ( -1 )

( inf & A ; gt ; 1 ) * ( inf-1 )

Investing growing

Population growing

C

0.819686

-0.599990

-0.365668

0.189027

-2.365807

10.68969

0.331024

0.307544

0.192164

0.029116

0.342021

0.677976

2.476211

-1.950911

-1.902896

6.492182

-6.917141

15.76707

0.0178

0.0585

0.0647

0.0000

0.0000

0.0000

7.011681

GROWTH t = 10.6897 + 0.8197INF t – 0.56INF T -1- 0.3657DUMMY

– 2.3658POP T + 0.189INV T

OLS Se = ( 1.1474 ) ( 0.3303 ) ( 0.2823 ) ( 0.199 )

( 0.5222 ) ( 0.039 )

t-stat = ( 9.3162 ) ( 2.7025 ) ( -2.1257 ) ( -1.8379 )

( -4.5307 ) ( 4.8365 )

White Se = ( 0.678 ) ( 0.331 ) ( 0.3075 ) ( 0.1922 )

( 0.342 ) ( 0.0291 )

t-stat = ( 15.767 ) ( 2.4762 ) ( -1.951 ) ( -1.9029 )

( -6.9171 ) ( 6.4922 )

The predating consequences show that White ‘s Heteroscedasticity-Consistent Variance and standard mistakes are considerable larger for rising prices rate and old twelvemonth rising prices rate, therefore the estimated T values are much smaller than those obtained by OLS. However, the standard mistakes for threshold degree of rising prices, population growing and existent investing are smaller than OLS end product, therefore the estimated T values are larger than OLS end product. Besides, both calculators are statistically important at the 10 per centum degree.