Assignment 4
FIN 690
This assignment must be submitted as a single Microsoft Word docx file through the
assignment link in this module. Download this file and rename it to follow the
naming convention LastNameFirstInitialAssigt4.docx. For example, if I were
submitting an exam, it would be named NelsonHAssig1.docx.
General requirements for all assignments
 You must use Eviews to complete the assignment. The Eviews output must
be copy-pasted into your assignment.
 Work done in Excel is not acceptable.
 You must use the equation editor to write any equations which may be
required in your submission. Equations written in plain text are not
acceptable.
 You must provide explanatory material interwoven with whatever tables,
equations and/or graphs are needed.
 Place your work in the designated locations.
Assigned Problems
Q4.1
Replicate the regression output on page 159 of the text and place your output here.
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
Obs*R-squared
1.497460
15.15657
Prob. F(10,234)
Prob. Chi-Square(10)
0.1410
0.1265
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Date: 07/14/14 Time: 00:30
Sample: 1986M05 2007M04
Included observations: 252
Presample missing value lagged residuals set to zero.
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
ERSANDP
DPROD
DCREDIT
DINFLATION
DMONEY
DSPREAD
RTERM
RESID(-1)
RESID(-2)
RESID(-3)
RESID(-4)
RESID(-5)
RESID(-6)
RESID(-7)
RESID(-8)
RESID(-9)
RESID(-10)
0.087053
-0.021725
-0.036054
-9.64E-06
-0.364149
0.225441
0.202672
-0.199640
-0.126780
-0.063949
-0.038450
-0.120761
-0.126731
-0.090371
-0.071404
-0.119176
-0.138430
-0.060578
1.461517
0.204588
0.510873
0.000162
3.010661
0.718175
13.70006
3.363238
0.065774
0.066995
0.065536
0.065906
0.065253
0.066169
0.065761
0.065926
0.066121
0.065682
0.059563
-0.106187
-0.070573
-0.059419
-0.120953
0.313909
0.014794
-0.059360
-1.927509
-0.954537
-0.586694
-1.832335
-1.942152
-1.365755
-1.085803
-1.807717
-2.093571
-0.922301
0.9526
0.9155
0.9438
0.9527
0.9038
0.7539
0.9882
0.9527
0.0551
0.3408
0.5580
0.0682
0.0533
0.1733
0.2787
0.0719
0.0374
0.3573
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.060145
-0.008135
13.80959
44624.90
-1009.826
0.880859
0.597301
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
-1.94E-16
13.75376
8.157352
8.409454
8.258793
2.013727
Q4.2
Then answer the question in the last sentence on page 159.
The conclusion from both versions of the test in this case is that the null hypothesis
of no autocorrelation should not be rejected. Does this agree with the DW test
result?
The Durbin-Watson statistic is equal to 2.013727; when the DW is near 2 this means
there is little evidence of autocorrelation and the null hypothesis would not be
rejected.
Q4.3
Replicate the regression results on page 169 of the text and place your output here.
Dependent Variable: ERMSOFT
Method: Least Squares
Date: 07/14/14 Time: 02:28
Sample (adjusted): 1986M05 2007M04
Included observations: 252 after adjustments
White heteroskedasticity-consistent standard errors & covariance
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
ERSANDP
DPROD
DCREDIT
DINFLATION
DMONEY
DSPREAD
RTERM
FEB98DUM
FEB03DUM
-0.086606
1.547971
0.455015
-5.92E-05
4.913297
-1.430608
8.624895
6.893754
-69.14177
-68.24391
1.458963
0.182655
0.336288
0.000149
2.318278
0.762907
8.961837
2.561914
1.567592
1.415784
-0.059361
8.474848
1.353050
-0.397263
2.119373
-1.875206
0.962403
2.690861
-44.10699
-48.20221
0.9527
0.0000
0.1773
0.6915
0.0351
0.0620
0.3368
0.0076
0.0000
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.358962
0.335122
12.56643
38215.45
-990.2898
15.05697
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
-0.420803
15.41135
7.938808
8.078865
7.995164
2.142031
Q4.4
Read the second paragraph on page 170. Then add one additional dummy variable
to eliminate an outlying observation. How much improvement did you see?
40
Series: Residuals
Sample 1986M05 2007M04
Observations 252
30
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
20
10
Jarque-Bera
Probability
-3.69e-16
0.924395
24.32716
-58.99873
11.78726
-2.033978
11.05478
854.9910
0.000000
0
-60
-50
-40
-30
-20
-10
0
10
20
I created another table for ermsoft , then looked for another outlier which I found to
be April 1990 (-57). I then created a dummy variable for this and achieved this
result:
Dependent Variable: ERMSOFT
Method: Least Squares
Date: 07/14/14 Time: 02:43
Sample (adjusted): 1986M05 2007M04
Included observations: 252 after adjustments
White heteroskedasticity-consistent standard errors & covariance
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
ERSANDP
DPROD
DCREDIT
DINFLATION
DMONEY
DSPREAD
RTERM
FEB98DUM
FEB03DUM
APR90DUM
0.569156
1.496274
0.338132
-0.000112
4.074575
-1.324322
7.970906
6.954852
-68.96408
-68.16981
-58.50680
1.319854
0.170651
0.317837
0.000141
2.164847
0.760826
8.937300
2.569879
1.537527
1.424409
1.842768
0.431227
8.768054
1.063854
-0.791244
1.882154
-1.740638
0.891870
2.706295
-44.85391
-47.85831
-31.74942
0.6667
0.0000
0.2885
0.4296
0.0610
0.0830
0.3734
0.0073
0.0000
0.0000
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.415015
0.390742
12.02933
34873.84
-978.7605
17.09765
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
-0.420803
15.41135
7.855242
8.009304
7.917233
2.109565
The Bera-Jarque test had value of 855, showing it was moved farther away from a
normal distribution. My conclusion is that it is not possible to achieve normal
residual in this case and that three dummy variables it is too excessive under the
circumstances.