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Hi, I also need two different homework with this same problems, and please make sure the screenshots from Eviews are different because this’s show the time created. Thanks so much.Short answer the all questions and post the screenshot&code.

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Macro-Econometrics

Homework Assignment #9 (150 points)

This assignment is based on the data in the Excel worksheet HW_Data_Period_9.xlsx, which

is found on the course website. The data set contains 100 observations for three simulated

data series – X, Z and Y. These data were generated by the instructor based on a model

where all three variables are non-stationary and co-integrated. By construction, the three

data series have two common trends and one co-integrating relationship. However, in this

exercise, you will pretend that you are uncertain about these properties.

(a). Plot each of the three series. The data do not appear to be stationary. Based on what

you see in the plots, what sort of non-stationary model(s) would you consider as possible

candidates – trend stationary, unit root with drift, or unit root without drift?

(b). Conduct an augmented Dickey-Fuller test (as below) for each variable, using lag lengths

p of 0 (no lags) and 4 (four lags). Report your results for the γs in a short table. What do

you conclude?

p

yt = 0 + yt −1 + i yt −i

i =1

Now, impose the null of a unit root and test for the significance of the constant. What are

your results? Finally, re-test for a unit root but do not include a constant. What are your

results? Do they match with your assessments in (a)?

(c). Estimate the following long-run cointegrating relationships (estimated separately):

xt = 0 + 1 yt + 2 zt + e xt

yt = 0 + 1 xt + 2 zt + e yt

zt = 0 + 1 xt + 2 yt + e zt

Report your results for the βs in a table, along with the t-statistics. Why can’t you conduct

inference using these t-statistics?

(d). Using the 3 sets of residuals from part (c), test for stationarity ( you do not need to report

your results). Why can’t we use the usual DF tables to make inferences about stationarity?

Which table is appropriate for finding the correct critical values? Using a 5% critical value,

what is the correct critical value? What do you conclude?

(e). Now, estimate an error correction model using the error-term from the 1st equation in

part (c) and 1 lag of Δx, Δy, and Δz. The easiest way to do this is to estimate a VAR model,

and enter the equilibrium errors as “exogenous variables”. Remember to lag them! Present

2

your results for the adjustment coefficients in a concise table with coefficient estimates and tstatistics. Which error-correction coefficients are statistically different from zero? What

critical value did you use? Are there any pitfalls in using that critical value?

(f) . Are the estimated error-correction coefficients consistent with LR convergence to your

estimated cointegration vectors in (c)? Why or why not? Please be specific in your answer

by carefully matching up each adjustment coefficients with the appropriate coefficient in the

LR relationship that you estimated in the first equation of part (c).

Observation

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X

Y

Z

10.97622

10.36175

10.21208

11.05946

11.00225

11.00590

11.69315

12.44415

13.51638

13.31938

8.46774

8.46653

9.01211

8.80450

9.30840

9.66332

10.07060

10.26112

10.19666

10.80520

11.70655

11.19843

11.35165

11.20021

11.23861

11.59986

11.43752

12.29081

11.88227

12.36887

12.20780

11.96558

12.33031

11.38093

11.51041

11.31334

11.58935

11.52128

10.79187

11.40603

11.87394

12.58821

13.02608

12.12935

11.59047

12.15770

3.61261

2.72729

2.35683

2.13419

2.10068

2.43712

2.33512

2.44850

2.49633

2.19629

2.39146

2.03542

1.85950

14.55768

14.54893

15.08455

15.29148

15.06824

14.90377

15.62939

15.30531

15.19888

15.07490

14.54948

14.03804

14.10188

12.59392

12.40027

11.98084

12.10963

12.21559

11.85499

12.25863

12.00730

11.70298

12.22569

11.20497

11.81487

10.45615

11.05662

10.67865

9.96496

10.35177

10.11335

9.78806

2.45490

2.28694

2.77243

2.35431

3.03748

1.75084

1.55597

0.76154

-0.13732

0.27688

0.36151

0.66424

0.41928

0.15659

-0.04445

-0.13138

-0.46595

-0.69594

-0.85779

-0.85776

-1.75480

-1.99197

-1.09848

11.92282

-0.69990

12.38016

12.20891

11.11620

10.22104

9.93698

-0.43094

-0.30860

-1.23864

-0.99660

0.17391

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10.12925

9.78474

9.58402

8.60145

8.93867

8.87682

8.57076

8.64115

8.23852

7.84874

7.98167

7.67769

8.38609

9.46876

10.32661

11.09066

10.66985

9.80061

9.63040

9.22397

8.43527

8.31355

8.35418

8.58732

7.91625

7.14858

7.47319

7.59959

7.71924

7.57745

6.94656

7.12050

6.82394

6.66223

6.53088

5.44104

5.91032

5.07667

6.42377

7.03724

7.52043

8.08969

8.51701

7.98976

10.47638

10.64500

9.80839

9.53916

9.08081

9.06441

8.15357

8.39691

7.81284

8.19854

8.69973

8.97249

10.66823

10.33500

9.87225

10.48771

10.12681

10.26314

9.78792

10.14984

10.11193

9.48142

8.63092

10.05750

10.03781

9.51219

10.02366

10.65559

11.12611

10.92283

11.13593

10.78459

10.77933

9.92041

9.22174

9.03652

9.88970

9.63234

10.25373

10.02474

9.70820

8.99030

8.77157

9.64736

0.38063

0.20784

0.87792

1.03403

1.02055

1.47468

0.77017

0.70509

0.96100

0.22607

0.83794

0.37314

0.96346

0.81529

0.49275

-0.16963

-0.85872

-0.97789

-1.31473

-1.78810

-1.67562

-2.04418

-2.46480

-2.16792

-2.12837

-2.04899

-2.58328

-2.76174

-3.24749

-2.52495

-2.63193

-2.46975

-2.73178

-3.24416

-3.50917

-3.40035

-3.83662

-3.13069

-2.83926

-3.18801

-3.18814

-3.50856

-3.28926

-2.95148

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7.31687

7.10034

6.70995

6.48888

5.21980

6.01420

5.45317

6.08516

6.33267

6.43428

6.46118

6.02886

6.03173

5.46913

9.67459

8.85503

8.87229

8.48114

8.09071

8.62074

8.42572

9.61541

9.68000

10.72446

10.43646

10.19101

11.50584

11.13432

-3.19401

-2.42122

-2.23377

-2.25333

-2.79106

-2.22384

-2.89084

-2.72013

-2.36781

-2.62773

-3.69244

-3.68897

-3.93809

-4.58285

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