The McGraw-Hill Series Economics essentials of economics brue, McConnell, and Flynn Essentials of Economics

895
Residual
Actual
Residual Plot
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
63.8000 
63.9000 
64.0000
64.0000 
64.4000
64.8000 
65.3000 
65.6000 
65.9000 
66.5000 
66.5000 
66.2000 
66.4000 
66.3000 
66.6000 
66.6000 
66.8000 
67.1000 
67.1000 
67.1000 
67.2000 
56.9000 
66.6000
65.2097 
65.0004
63.6047 
63.5173 
64.9131 
65.1566 
65.2347 
65.8842 
66.4103 
66.6148 
66.5819 
65.8745 
65.4608 
65.8917 
66.4147 
66.7644 
66.8425 
67.0097 
66.9974 
67.0443 
67.1364 
66.4589 
65.5770
1.40974 
1.10044 
0.39535 
0.48268 
0.51311 
0.35664 
0.06526 
0.28416 
0.51027 
0.11476 
0.08186 
0.32546 
0.93923 
0.40834 
0.18530 
0.16441 
0.04251 
0.09032 
0.10263 
0.05569 
0.06355 
0.44105 
1.02304
Dependent Variable: CLFPR
Method: Least Squares
Variable
Coefficient
t
-Statistic
Prob.
C
CUNR
AHE82
80.90133
0.671348
1.404244
Std. Error
4.756195
0.082720
0.608615
17.00967
8.115928
2.307278
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin–Watson stat
0.772765
0.750042
0.584308
6.828312
18.66979
0.787625
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F
-statistic
Prob(
F
-statistic)
65.89565
1.168713
1.884330
2.032438
34.00731
0.000000
Sample: 1980–2002
Included observations: 23
0.0000
0.0000
0.0319
Obs
Fitted
1.0
0.5
0.0
0.5
1.0
1.5
7
6
5
4
3
2
1
0
Series: Residuals
Sample 1980–2002
Observations 23
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
1.39e-14
0.063552 
1.023040 
1.409735 
0.557116 
0.593013 
3.752631
Jarque–Bera
Probability
1.890898
0.388505
FIGURE E.1
EViews
output of
civilian labor force
participation
regression.
guj75772_appE.qxd 05/09/2008 11:10 AM Page 895

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896
Appendix E
Computer Output of EViews, MINITAB, Excel, and STATA
the error sum of squares, in ML we try to find those estimators that maximize the possi-
bility of observing the sample at hand. Under the normality assumption of the error term,
OLS and ML give identical estimates of the regression coefficients. The Durbin–Watson
statistic is used to find out if there is first-order serial correlation in the error terms.
The second part of the 
EViews
output gives the actual and fitted values of the dependent
variable and the difference between the two, which represent the residuals. These residuals
are plotted alongside this output with a vertical line denoting zero. Points to the right of the
vertical line are positive residuals and those to the left represent negative residuals.
The third part of the output gives the histogram of the residuals along with their sum-
mary statistics. It gives the Jarque–Bera (JB) statistic to test for the normality of the error
terms and also gives the probability of obtaining the stated statistics. The higher the prob-
ability of obtaining the observed JB statistic, the greater is the evidence in favor of the null
hypothesis that the error terms are normally distributed.
Note that 
EViews
does not give directly the analysis-of-variance (ANOVA) table, but it
can be constructed easily from the data on the residual sum of squares, the total sum of
squares (which will have to be derived from the standard deviation of the dependent
variable), and their associated degrees of freedom. The 
F
value given from this exercise
should be equal to the 
F
value reported in the first part of the table.