Economics 4312: Business Cycles and Forecasting

Topics for Review from Chapters 1-4  (Exam I)

 

1.  Evaluate the following statement:  “subjective judgment has no role in business forecasting.”

 

2.  Distinguish between macro and micro forecasts and between top-down and bottom-up forecasting.

 

3.  What is involved in each of the following components of the forecasting process:

            a.  The objective and purpose of the forecast.

            b.  Data analysis

            c.  Model specification

            d.  Model evaluation

            e.  Monitoring the forecast

            f.  Presentation of the forecast to management

            g.  Initial forecast revision

 

4.  When did the most recent recession begin and how was this particular turning point determined?

 

5.  Describe the relationship between forecasting and business planning.  Include a statement of the roles of social and political factors and exogenous versus endogenous influences.

 

6.  Why is the change in GDP more useful to economists than the level of GDP?

 

7.  What are three summary leading indicators used to predict the aggregate business cycle?  Would you forecast be based exclusively on these measures?

 

8.  Why would the 1973-75 recession be characterized as an exogenous recession?  Would you classify the 1990-91 recession as more endogenous or exogenous?  Why? 

 

9.       What was the general sequence of events in the capital goods sector that led up to the 2001 economic recession?

 

 

10.  Discuss how monthly sales data can be adjusted for the number of working days.

 

11.  Suppose you had a furniture store in Waco Texas and gathered monthly data on sales.  Categorize the trend, seasonal, cyclical, and irregular forces that you might expect to affect your sales.

 

12.  If you collected annual data for your furniture store, how would you separate the trend influences from the cyclical-irregular influences?  Illustrate mathematically using a multiplicative model to determine the predicted trend and the CI relative.

 

13.  If you were not sure that your annual data experienced a linear or a non-linear trend, how would you decide on which trend equation to use?

 

14.  How can your annual data be used to determine a forecast for next year?  Would your forecast based upon the trend equation be adjusted if its R-squared is 75 percent?  Why?

 

15.  Why are seasonal indexes needed to determine monthly forecasts for your furniture store?

Suppose the seasonal factor in January is 95.5 and a forecast of seasonally adjusted sales for January is $12.9 thousand.  What would be the forecast of actual sales for January?

 

16.  The attached Excel output is based upon annual retail sales data for Waco from 1988 to 2000.

 

  1. Write the equation of the trend line.
  2. Does a significant positive trend exist?  How do you know?
  3. How much (what percentage) of the variation in sales is “explained” by trend factors?  How much is due to cyclical-irregular factors?
  4. How is the trend column computed based upon the regression coefficients for each of the time periods?
  5. Compute a column of CI relatives for each of the 3-6 time periods.  If 1990-91 is a recession period, is your business pro-cyclical or counter-cyclical?
  6. What is the forecast for 2001 based upon trend forces only?  Would you be comfortable with this forecast knowing what you do about the importance of cyclical forces?
  7. Suppose you believe that 2001 will be a poor year and your sales will be 2 percent below the trend projection.  What is your assumed CI relative?  What is your forecast based upon this assumption?

 

17.   Suppose you have a furniture store in Waco and you used a model to forecast your sales. Suppose the results of your forecasting model are as follows:

 

Period     Sales     Forecast       errors              

                1        $200          185

                2           225          230

                3           235          220

                4           220          210

                5           230          220

 

  1. Determine the ex post error terms for each of the five periods.
  2. Plot a turning-point error diagram.  Are there any missed or false signals given by your model?
  3. Compute the mean squared error and the root mean squared error.
  4. Computed the mean absolute error; under what conditions would it be preferred over the MSE.
  5. Compute the mean absolute percentage error.  (Note the root percent mean square could also be used, but it has the exact same interpretation and is less frequently used.)
  6. Compute the mean error.  Do you have any indication of bias in your model?
  7. If you had to choose between this model and an alternative model, what criteria would you use?

 

Excel Output for Waco Annual Retail Sales

Year

Sales

X

 

 

 

 

 

 

1987

1252635

1

 

 

 

 

 

 

1988

1291555

2

 

 

 

 

 

 

1989

1367666

3

 

 

 

 

 

 

1990

1407654

4

 

 

 

 

 

 

1991

1450903

5

 

 

 

 

 

 

1992

1554892

6

 

 

 

 

 

 

1993

1686952

7

 

 

 

 

 

 

1994

1815047

8

 

 

 

 

 

 

1995

2030099

9

 

 

 

 

 

 

1996

2080214

10

 

 

 

 

 

 

1997

2150987

11

 

 

 

 

 

 

1998

2214242

12

 

 

 

 

 

 

1999

2309949

13

 

 

 

 

 

 

2000

2449341

14

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

SUMMARY OUTPUT

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Regression Statistics

 

 

 

 

 

 

 

Multiple R

0.989572

 

 

 

 

 

 

 

R Square

0.979253

 

 

 

 

 

 

 

Adjusted R Square

0.977524

 

 

 

 

 

 

 

Standard Error

61680.86

 

 

 

 

 

 

 

Observations

14

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ANOVA

 

 

 

 

 

 

 

 

 

df

SS

MS

F

Significance F

 

 

 

Regression

1

2.15E+12

2.2E+12

566.3932

1.82E-11

 

 

 

Residual

12

4.57E+10

3.8E+09

 

 

 

 

 

Total

13

2.2E+12

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

1060225

34820

30.4487

9.87E-13

984358.3

1136091

984358.3

1136091

X

97323.73

4089.402

23.799

1.82E-11

88413.69

106233.8

88413.69

106233.8