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

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

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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