1. Demonstrate your understanding of the consumption function’s role in macroeconomic
theory. 2. Demonstrate your ability to perform data (regression) analysis in either Excel. 3. Demonstrate your ability to interpret regression results. 4. Demonstrate your ability to tie empirical results to macroeconomic theory. Specific Steps:
1. Download consumption function dataset (Dataset-CF.xls) and notes on variables in the
dataset from BlackBoard.
2. Review data set notes on variable in the consumption function dataset to familiarize
yourself with the variables from the Notes – CF Dataset.PDF. 3. Open dataset in Excel.
4. Review (if necessary) the theoretical relationship between variables in the consumption
function dataset. 5. Develop a priori hypothesis regarding the coefficients for the individual betas for the
variables in your regression.
Factors to consider when performing assignment: 1. Interest rate (What type of interest rate is this?) 2. Units of variables used in the regression. Deliverables: 1. A brief description of the consumption function, and its significance to the study of
economics. 2. E-mail your expected (a priori) theoretical relationship for the variables (betas) you will
use in your regressions. 3. Output of regression analysis (either in or Excel). 4. Interpretation of results, with a discussion of how well your results related to the testable
hypothesis (a priori assumptions) you developed.
interpreting_regression_output.pdf
dataset_cf__28no_labels_29.xls
dataset_cf__28no_labels_29.xls
interpreting_regression_output.pdf
notes___cf_dataset.pdf
Unformatted Attachment Preview
INTERPRETING REGRESSION OUTPUT
Coefficient of
Determination (R2):
A number between 0.0 and 1.0
that expresses the amount of
variance in one variable that is
explained by one or more other
variables.
Adjusted Coefficient of
Determination (R2):
In mutiple regression the
Adjusted R2 compensates for
additional expalantory
variables added to the
regression.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.9984
R Square
0.9967
Adjusted R Square
0.9967
Standard Error
86.4344
Observations
54
Standard Error of
Regression:
The standard error of the
regression is the estimate
of the accuracy of the
prediction for the
regression equation.
F Statistic:
The F stat provides a numeric
value that descibes the statistical
significance of the regression
results for all variables.
Significance F:
Provides the degree of certainty
(confidence level) associated with the F
Statistic. (The smaller the number the
more significant the F stat.)
Signifcance F:
In exponential form we can
see that the significance F is a
very small number bu not
quite zero.
Sum of Square and Mean
Sum of Squares:
If you want to learn about
these numbers, take
econometrics.
2.3164682249284E-66
ANOVA
df
Regression
Residual
Total
SS
MS
F
1 119,005,076.0123 119,005,076.0123
52
388,487.3210
7,470.9100
53 119,393,563.3333
Significance F
15,929.1272
0.0000
COEFFICIENTS
Coefficients
Intercept
Disposable Income (Yd)
-62.2106
0.9176
Coefficients:
The coefficients correspond to the
value of our estimates for our
dependent variable. Any part of our
regression equation that is not
explained by the independent variables
is assigned to the intersept term.
Standard Error
26.1703
0.0073
t Stat
P-value
-2.3771
126.2106
T Stat:
This T stat corresponds to the
estimated Beta from our regression,
which you can look up in a student’s
t table to see what the probability is
that it lies where it does. (Or you
can just look at the P-value that
corrsponds to the t-stat.
Lower 95%
0.0212
0.0000
2.3164682249284E-66
P Value in Exponential Form:
The P Value of our T statistic for our
explanatory variabel (Disposable Income) is
a very low number. In fact the P-value is
the same as the value for the Significance F
because the regression we ran is a single
variable model.
-114.7252
0.9030
Upper 95%
-9.6960
0.9322
95% Confidence Interval:
The 95% confidence interval
that is constructed around the
regression estimates for our
betas, tell us the range in
which we can expect the true
Beta to lie.
File saved as: F:2017-01-12I-DocumentsAcademicSDSUEconomicsEcon 320 (Intermediate Macro)Data AssignmentInterpreting Regression Output.doc
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
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1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
976.4
998.1
1025.3
1090.9
1107.1
1142.4
1197.2
1221.9
1310.4
1348.8
1381.8
1393.0
1470.7
1510.8
1541.2
1617.3
1684.0
1784.8
1897.6
2006.1
2066.2
2184.2
2264.8
2317.5
2405.2
2550.5
2675.9
2653.7
2710.9
2868.9
2992.1
3124.7
3203.2
3193.0
3236.0
3275.5
3454.3
3640.6
3820.9
3981.2
4113.4
4279.5
4393.7
4474.5
4466.6
4594.5
4748.9
4928.1
5075.6
5237.5
5423.9
5683.7
1035.2
1090.0
1095.6
1192.7
1227.0
1266.8
1327.5
1344.0
1433.8
1502.3
1539.5
1553.7
1623.8
1664.8
1720.0
1803.5
1871.5
2006.9
2131.0
2244.6
2340.5
2448.2
2524.3
2630.0
2745.3
2874.3
3072.3
3051.9
3108.5
3243.5
3360.7
3527.5
3628.6
3658.0
3741.1
3791.7
3906.9
4207.6
4347.8
4486.6
4582.5
4784.1
4906.5
5014.2
5033.0
5189.3
5261.3
5397.2
5539.1
5677.7
5854.5
6168.6
5166.8
5280.8
5607.4
5759.5
6086.1
6243.9
6355.6
6797.0
7172.2
7375.2
7315.3
7870.0
8188.1
8351.8
8971.9
9091.5
9436.1
10003.4
10562.8
10522.0
11312.1
12145.4
11672.3
11650.0
12312.9
13499.9
13081.0
11868.8
12634.4
13456.8
13786.3
14450.5
15340.0
15965.0
15965.0
16312.5
16944.8
17526.7
19068.3
20530.0
21235.7
22332.0
23659.8
23105.1
24050.2
24418.2
25092.3
25218.6
27439.7
29448.2
32664.1
35587.0
-10.351
-4.720
1.044
0.407
-5.283
-0.277
0.561
-0.138
0.262
-0.736
-0.261
-0.575
2.296
1.511
1.296
1.396
2.058
2.027
2.112
2.020
1.213
1.055
1.732
1.166
-0.712
-0.156
1.414
-1.043
-3.534
-0.657
-1.190
0.113
1.704
2.298
4.704
4.449
4.691
5.848
4.331
3.768
2.819
3.287
4.318
3.595
1.803
1.007
0.625
2.206
3.333
3.083
3.120
3.584
1999
2000
5968.4
6257.8
6320.0
6539.2
39591.3
38167.7
3.245
3.576
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
976.4
998.1
1025.3
1090.9
1107.1
1142.4
1197.2
1221.9
1310.4
1348.8
1381.8
1393.0
1470.7
1510.8
1541.2
1617.3
1684.0
1784.8
1897.6
2006.1
2066.2
2184.2
2264.8
2317.5
2405.2
2550.5
2675.9
2653.7
2710.9
2868.9
2992.1
3124.7
3203.2
3193.0
3236.0
3275.5
3454.3
3640.6
3820.9
3981.2
4113.4
4279.5
4393.7
4474.5
4466.6
4594.5
4748.9
4928.1
5075.6
5237.5
5423.9
5683.7
1035.2
1090.0
1095.6
1192.7
1227.0
1266.8
1327.5
1344.0
1433.8
1502.3
1539.5
1553.7
1623.8
1664.8
1720.0
1803.5
1871.5
2006.9
2131.0
2244.6
2340.5
2448.2
2524.3
2630.0
2745.3
2874.3
3072.3
3051.9
3108.5
3243.5
3360.7
3527.5
3628.6
3658.0
3741.1
3791.7
3906.9
4207.6
4347.8
4486.6
4582.5
4784.1
4906.5
5014.2
5033.0
5189.3
5261.3
5397.2
5539.1
5677.7
5854.5
6168.6
5166.8
5280.8
5607.4
5759.5
6086.1
6243.9
6355.6
6797.0
7172.2
7375.2
7315.3
7870.0
8188.1
8351.8
8971.9
9091.5
9436.1
10003.4
10562.8
10522.0
11312.1
12145.4
11672.3
11650.0
12312.9
13499.9
13081.0
11868.8
12634.4
13456.8
13786.3
14450.5
15340.0
15965.0
15965.0
16312.5
16944.8
17526.7
19068.3
20530.0
21235.7
22332.0
23659.8
23105.1
24050.2
24418.2
25092.3
25218.6
27439.7
29448.2
32664.1
35587.0
-10.351
-4.720
1.044
0.407
-5.283
-0.277
0.561
-0.138
0.262
-0.736
-0.261
-0.575
2.296
1.511
1.296
1.396
2.058
2.027
2.112
2.020
1.213
1.055
1.732
1.166
-0.712
-0.156
1.414
-1.043
-3.534
-0.657
-1.190
0.113
1.704
2.298
4.704
4.449
4.691
5.848
4.331
3.768
2.819
3.287
4.318
3.595
1.803
1.007
0.625
2.206
3.333
3.083
3.120
3.584
1999
2000
5968.4
6257.8
6320.0
6539.2
39591.3
38167.7
3.245
3.576
INTERPRETING REGRESSION OUTPUT
Coefficient of
Determination (R2):
A number between 0.0 and 1.0
that expresses the amount of
variance in one variable that is
explained by one or more other
variables.
Adjusted Coefficient of
Determination (R2):
In mutiple regression the
Adjusted R2 compensates for
additional expalantory
variables added to the
regression.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.9984
R Square
0.9967
Adjusted R Square
0.9967
Standard Error
86.4344
Observations
54
Standard Error of
Regression:
The standard error of the
regression is the estimate
of the accuracy of the
prediction for the
regression equation.
F Statistic:
The F stat provides a numeric
value that descibes the statistical
significance of the regression
results for all variables.
Significance F:
Provides the degree of certainty
(confidence level) associated with the F
Statistic. (The smaller the number the
more significant the F stat.)
Signifcance F:
In exponential form we can
see that the significance F is a
very small number bu not
quite zero.
Sum of Square and Mean
Sum of Squares:
If you want to learn about
these numbers, take
econometrics.
2.3164682249284E-66
ANOVA
df
Regression
Residual
Total
SS
MS
F
1 119,005,076.0123 119,005,076.0123
52
388,487.3210
7,470.9100
53 119,393,563.3333
Significance F
15,929.1272
0.0000
COEFFICIENTS
Coefficients
Intercept
Disposable Income (Yd)
-62.2106
0.9176
Coefficients:
The coefficients correspond to the
value of our estimates for our
dependent variable. Any part of our
regression equation that is not
explained by the independent variables
is assigned to the intersept term.
Standard Error
26.1703
0.0073
t Stat
P-value
-2.3771
126.2106
T Stat:
This T stat corresponds to the
estimated Beta from our regression,
which you can look up in a student’s
t table to see what the probability is
that it lies where it does. (Or you
can just look at the P-value that
corrsponds to the t-stat.
Lower 95%
0.0212
0.0000
2.3164682249284E-66
P Value in Exponential Form:
The P Value of our T statistic for our
explanatory variabel (Disposable Income) is
a very low number. In fact the P-value is
the same as the value for the Significance F
because the regression we ran is a single
variable model.
-114.7252
0.9030
Upper 95%
-9.6960
0.9322
95% Confidence Interval:
The 95% confidence interval
that is constructed around the
regression estimates for our
betas, tell us the range in
which we can expect the true
Beta to lie.
File saved as: F:2017-01-12I-DocumentsAcademicSDSUEconomicsEcon 320 (Intermediate Macro)Data AssignmentInterpreting Regression Output.doc
Econ 641L (Final Assignment)
Consumption Function (Dataset Notes)
Notes – Consumption Function Dataset
Variables:
Observations:
Dataset:
4
54
Time Series (Macroeconomic)
Dataset Variables
1.
2.
3.
4.
5.
Year = calendar year
Con = real consumption expenditures in billions of chained 1996 dollars
Yd = real personal disposable income in billions of chained 1996 dollars
Wealth = real wealth in billions of chained 1996 dollars
Interest = nominal annual yield on 3-month Treasury securities – inflation rate
(measured by the annual % change in annual chained price index)
Notes:
The nominal real wealth variable was created using data from the Federal Reserve Board’s
measure of end-of-year net worth for households and nonprofits in the flow of funds accounts.
The price index used to convert this nominal wealth variable to a real wealth variable was the
average of the chained price index from the 4th quarter of the current year and the 1st quarter of
the subsequent year.
Data Sources:
C, Yd, and quarterly and annual chain-type price indexes (1996=100) – Bureau of Economic
Analysis, U.S. Department of Commerce (http://www.bea.doc.gov/bea/dn1.htm)
Nominal annual yield on 3-month Treasury securities – Economic Report of the
President, 2002
Nominal wealth = end of year nominal net worth of households and non-profits (from Federal
Reserve flow of funds data: http://www.federalreserve.gov)
File saved as: F:2017-01-12I-DocumentsAcademicSDSUEconomicsEcon 641L (Stats Lab)AssignmentsNotes
– CF Dataset.doc
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