# Chapter 5 For studying demand relationships for

Document Type

Test Prep

Book Title

Managerial Economics: Applications-- Strategies and Tactics (Upper Level Economics Titles) 13th Edition

Authors

Frederick H.deB. Harris, James R. McGuigan, R. Charles Moyer

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Test Bank Chapter 5

Chapter 5—Business and Economic Forecasting

MULTIPLE CHOICE

1. Time-series forecasting models:

a.

are useful whenever changes occur rapidly and wildly

b.

are more effective in making long-run forecasts than short-run forecasts

c.

are based solely on historical observations of the values of the variable being forecasted

d.

attempt to explain the underlying causal relationships which produce the observed

outcome

e.

none of the above

2. The forecasting technique which attempts to forecast short-run changes and makes use of economic

indicators known as leading, coincident or lagging indicators is known as:

a.

econometric technique

b.

time-series forecasting

c.

opinion polling

d.

barometric technique

e.

judgment forecasting

3. The use of quarterly data to develop the forecasting model Yt = a +bYt−1 is an example of which

forecasting technique?

a.

Barometric forecasting

b.

Time-series forecasting

c.

Survey and opinion

d.

Econometric methods based on an understanding of the underlying economic variables

involved

e.

Input-output analysis

4. Variations in a time-series forecast can be caused by:

a.

cyclical variations

b.

secular trends

c.

seasonal effects

d.

a and b only

e.

a, b, and c

5. The variation in an economic time-series which is caused by major expansions or contractions usually

of greater than a year in duration is known as:

a.

secular trend

b.

cyclical variation

c.

seasonal effect

d.

unpredictable random factor

e.

none of the above

6. The type of economic indicator that can best be used for business forecasting is the:

a.

leading indicator

b.

coincident indicator

c.

lagging indicator

d.

current business inventory indicator

e.

optimism/pessimism indicator

7. Consumer expenditure plans is an example of a forecasting method. Which of the general categories

best described this example?

a.

time-series forecasting techniques

b.

barometric techniques

c.

survey techniques and opinion polling

d.

econometric techniques

e.

input-output analysis

8. In the first-order exponential smoothing model, the new forecast is equal to a weighted average of the

old forecast and the actual value in the most recent period.

a.

true

b.

false

9. Simplified trend models are generally appropriate for predicting the turning points in an economic

time series.

a.

true

b.

false

10. Smoothing techniques are a form of ____ techniques which assume that there is an underlying pattern

to be found in the historical values of a variable that is being forecast.

a.

opinion polling

b.

barometric forecasting

c.

econometric forecasting

d.

time-series forecasting

e.

none of the above

11. Seasonal variations can be incorporated into a time-series model in a number of different ways,

including:

a.

ratio-to-trend method

b.

use of dummy variables

c.

root mean squared error method

d.

a and b only

e.

a, b, and c

12. For studying demand relationships for a proposed new product that no one has ever used before, what

would be the best method to use?

a. ordinary least squares regression on historical data

b. market experiments, where the price is set differently in two markets

c. consumer surveys, where potential customers hear about the product and are asked their opinions

d. double log functional form regression model

e. all of the above are equally useful in this case

13. Which of the following barometric indicators would be the most helpful for forecasting future sales for

an industry?

a. lagging economic indicators.

b. leading economic indicators.

c. coincident economic indicators.

d. wishful thinking

e. none of the above

14. An example of a time series data set is one for which the:

a. data would be collected for a given firm for several consecutive periods (e.g., months).

b. data would be collected for several different firms at a single point in time.

c. regression analysis comes from data randomly taken from different points in time.

d. data is created from a random number generation program.

d. use of regression analysis would impossible in time series.

15. Examine the plot of data.

Sales

Time

Test Bank Chapter 5

It is likely that the best forecasting method for this plot would be:

a. a two-period moving average

b. a secular trend upward

c. a seasonal pattern that can be modeled using dummy variables or seasonal adjustments

d. a semi-log regression model

e. a cubic functional form

16. Emma uses a linear model to forecast quarterly same-store sales at the local Garden Center. The

results of her multiple regression is:

Sales = 2,800 + 200•T - 350•D

where T goes from 1 to 16 for each quarter of the year from the first quarter of 2006 (‘06I) through the

fourth quarter of 2009 (‘09 IV). D is a dummy variable which is 1 if sales are in the cold and dreary

first quarter, and zero otherwise, because the months of January, February, and March generate few

sales at the Garden Center. Use this model to estimate sales in a store for the first quarter of 2010 in

the 17th month; that is: {2010 I}. Emma’s forecast should be:

a. 5,950

b. 6,200

c. 6,350

d. 6,000

e. 5,850

17. Select the correct statement.

a. Qualitative forecasts give the direction of change.

b. Quantitative forecasts give the exact amount or exact percentage change.

c. Diffusion forecasts use the proportion of the forecasts that are positive to forecast up or down.

d. Surveys are a form of qualitative forecasting.

e. all of the above are correct.

18. If two alternative economic models are offered, other things equal, we would

a. tend to pick the one with the lowest R2.

b. select the model that is the most expensive to estimate.

c. pick the model that was the most complex.

d. select the model that gave the most accurate forecasts

e. all of the above

19. Mr. Geppetto uses exponential smoothing to predict revenue in his wood carving business. He uses a

weight of = .4 for the naïve forecast and (1-) = .6 for the past forecast. What revenue did he pre-

dict for March using the data below? Select closet answer.

MONTH REVENUE FORECAST

Nov 100 100

Dec 90 100

Jan 115 ----

Feb 110 ----

MARCH ? ?

a. 106.2

b. 104.7

c. 103.2

d. 102.1

e. 101.7

20. Suppose a plot of sales data over time appears to follow an S-shape as illustrated below.

Sales

Time

Which of the following is likely that the best forecasting functional form to use for sales data above?

a. A linear trend, Sales = a + b T

b. A quadratic shape in T, using T-squared as another variable, Sales = a + b T + cT2.

c. A semi-log form as sales appear to be growing at a constant percentage rate, Ln Sales = a + bT

d. A cubic shape in T, using T-squared and T-cubed as variables, Sales = a + b T + cT2 + d T3.

e. A quadratic shape in T and T-squared as variables, Sales = a + b T + cT2

PROBLEM

1. The Accuweather Corporation manufactures barometers and thermometers for weather forecasters. In

an attempt to forecast its future needs for mercury, Accuweather's chief economist estimated average

monthly mercury needs as:

N = 500 + 10X

Test Bank Chapter 5

where N = monthly mercury needs (units) and X = time period in months (January 2008= 0). The

following monthly seasonal adjustment factors have been estimated using data from the past five

years:

Month

Adjustment Factor

January

15%

April

10%

July

−20%

September

5%

December

−10%

(a)

Forecast Accuweather's mercury needs for January, April, July, September, and

December of 2010.

(b)

The following actual and forecast values of mercury needs in the month of November

have been recorded:

Year

Actual

Forecast

2008

456

480

2009

324

360

2007

240

240

What seasonal adjustment factor should the firm use for November?

2. Milner Brewing Company experienced the following monthly sales (in thousands of barrels) during

2010:

Jan.

Feb.

Mar.

Apr.

May

June

100

92

112

108

116

116

(a)

Develop 2-month moving average forecasts for March through July.

(b)

Develop 4-month moving average forecasts for May through July.

(c)

Develop forecasts for February through July using the exponential smoothing method

(with w = .5). Begin by assuming .

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