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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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