Forecasting and Predictive Analytics with Forecast X Barry Keating 7e - Test Bank

Forecasting and Predictive Analytics with Forecast X Barry Keating 7e – Test Bank

 

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Forecasting and Predictive Analytics with Forecast X, 7e (Keating)

Chapter 6   Explanatory Models 2. Time-Series Decomposition

 

1) The time-series decomposition model is best described as a

1. A) ratio-to-exponential smoothing technique.
2. B) ratio-to-moving average technique.
3. C) multiplicative moving average technique.
4. D) moving average factorization technique.
5. E) None of the options are correct.

 

Answer:  B

Difficulty: 1 Easy

Topic:  Introduction

Learning Objective:  6-01 Explain the similarity between how time series decomposition and Winters exponential smoothing deal with seasonality.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

2) Which of the following is not a reason why time-series decomposition has gained favor with forecasters and their managers?

1. A) Forecast accuracy
2. B) Ease in understanding
3. C) Very little computation is required.
4. D) Time-series decomposition resembles the way many managers analyze the future.
5. E) None of the options are correct.

 

Answer:  C

Difficulty: 1 Easy

Topic:  Introduction

Learning Objective:  6-01 Explain the similarity between how time series decomposition and Winters exponential smoothing deal with seasonality.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

3) Which of the following is not a technique used to generate forecasts with time series decomposition?

1. A) Moving averages
2. B) Trend projection
3. C) Multiplicative seasonality
4. D) Dummy variables
5. E) All of the options are correct.

 

Answer:  D

Difficulty: 1 Easy

Topic:  The Basic Time-Series Decomposition Model

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

4) In time-series decomposition analysis, “decomposition” refers to

1. A) converting an annual trend line into a monthly trend line.
2. B) deseasonalizing the data.
3. C) separating a time series into component parts.
4. D) isolating the cyclical component of a time series.
5. E) None of the options are correct.

 

Answer:  C

Difficulty: 1 Easy

Topic:  The Basic Time-Series Decomposition Model

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

5) Which of the following is not a component in the time series decomposition model?

1. A) Trend
2. B) Seasonal variation
3. C) Irregular variation
4. D) Business indicators
5. E) Cyclical variation

 

Answer:  D

Difficulty: 1 Easy

Topic:  The Basic Time-Series Decomposition Model

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

6) Which forecasting model identifies and forecasts component factors that influence the level of a time series?

1. A) Event model
2. B) Time series decomposition
3. C) Moving average smoothing
4. D) Exponential smoothing
5. E) Winter’s smoothing

 

Answer:  B

Difficulty: 1 Easy

Topic:  The Basic Time-Series Decomposition Model

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

7) Which of the following best describes the general approach to forecasting when actually applying time-series decomposition?

1. A) Y = T + S + C + I
2. B) Y = T × S × C × I
3. C) Y = T × S × C
4. D) Y = (T + C) × S
5. E) None of the options are correct.

 

Answer:  C

Difficulty: 1 Easy

Topic:  The Basic Time-Series Decomposition Model

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

8) Time series decomposition models seasonality

1. A) using dummy variables.
2. B) in an additive fashion.
3. C) in an exponential manner.
4. D) similar to Winter’s smoothing.
5. E) None of the options are correct.

 

Answer:  D

Difficulty: 1 Easy

Topic:  Introduction

Learning Objective:  6-01 Explain the similarity between how time series decomposition and Winters exponential smoothing deal with seasonality.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

9) Which of the following is not correct about using moving averages to deseasonalize a time series?

1. A) The number of periods in the average should reflect the number of seasons.
2. B) The number of periods for annual data should be 12.
3. C) The number of periods for quarterly data should be 4.
4. D) The moving average is interpreted as the typical level of a variable in a given year.
5. E) All of the options are correct.

 

Answer:  B

Difficulty: 1 Easy

Topic:  Deseasonalizing the Data and Finding the Indices

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

10) Deseasonalizing the data using moving averages

1. A) removes the seasonal component of a time series.
2. B) removes the irregular component of a time series.
3. C) preserves the cyclical component of a time series.
4. D) preserves the trend component of a time series.
5. E) All of the options are correct.

 

Answer:  E

Difficulty: 1 Easy

Topic:  Deseasonalizing the Data and Finding the Indices

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

11) When calculating centered moving averages, how many data points are lost for a given time series when a n-period moving average is used?

1. A) n points at the beginning
2. B) n points at the end
3. C) n points on both ends
4. D) sample size − n points at the beginning
5. E) None of the options are correct.

 

Answer:  E

Difficulty: 1 Easy

Topic:  Deseasonalizing the Data and Finding the Indices

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

12) When calculating centered moving-averages using a 4-period moving average, how many data points are lost at the beginning of the original series?

1. A) 1
2. B) 2
3. C) 3
4. D) 4
5. E) None of the options are correct.

 

Answer:  B

Difficulty: 1 Easy

Topic:  Deseasonalizing the Data and Finding the Indices

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

13) When calculating centered moving-averages using a 4-period moving average, how many data points are lost at both ends of the original series?

1. A) 1
2. B) 2
3. C) 3
4. D) 4
5. E) None of the options are correct.

 

Answer:  B

Difficulty: 1 Easy

Topic:  Deseasonalizing the Data and Finding the Indices

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

14) In time-series decomposition, seasonal factors are calculated by

1. A) SFt= (Yt) × (CMAt).
2. B) SFt= Yt/CMA
3. C) (CMAt) × (SFt) = Y
4. D) SFt= Yt− CMA
5. E) None of the options are correct.

 

Answer:  B

Difficulty: 1 Easy

Topic:  Deseasonalizing the Data and Finding the Indices

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

15) A seasonal index number of .80 for quarter one in a time series decomposition model of an automobile parts manufacturer suggests

1. A) quarter one sales are 80% above the norm.
2. B) quarter one sales are 1.80% below the norm.
3. C) quarter one sales are 20% below the norm.
4. D) quarter one sales are 80% below the norm.
5. E) None of the options are correct.

 

Answer:  C

Difficulty: 1 Easy

Topic:  Deseasonalizing the Data and Finding the Indices

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

16) The sum of seasonal index numbers should equal

1. A) one.
2. B) sample size/2.
3. C) number of seasons.
4. D) 12.
5. E) None of the options are correct.

 

Answer:  C

Difficulty: 1 Easy

Topic:  Deseasonalizing the Data and Finding the Indices

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

17) The sum of seasonal index numbers for monthly data should equal

1. A) one.
2. B) sample size/2.
3. C) 4.
4. D) 12.
5. E) None of the options are correct.

 

Answer:  D

Difficulty: 1 Easy

Topic:  Deseasonalizing the Data and Finding the Indices

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

18) Quarter one sales for a tire manufacturer were $120,000,000. If the quarter one seasonal index was 1.20 in a time series decomposition model, what is an estimate of annual sales for this firm?

1. A) $100,000,000
2. B) $144,000,000
3. C) $400,000,000
4. D) $576,000,000
5. E) None of the options are correct.

 

Answer:  C

Difficulty: 2 Medium

Topic:  The Time Series Decomposition Forecast

Learning Objective:  6-04 Explain how one determines the long-term trend for time-series decomposition.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

19) Suppose Nike sales are expected to be 1.2 billion dollars for the year 2005. If the January seasonal index for Nike is 0.98, what is a reasonable estimate for January 2005 sales revenue?

1. A) 0.098 billion
2. B) 0.1 billion
3. C) 1.176 billion
4. D) 2.18 billion

 

Answer:  A

Difficulty: 2 Medium

Topic:  The Time Series Decomposition Forecast

Learning Objective:  6-04 Explain how one determines the long-term trend for time-series decomposition.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

20) People’s Bank

 

Seasonal Indexes of sales revenue of People’s Bank are:

 

January		1.20
February		0.90
March		1.00
April		1.08
May		1.02
June		1.10
July		1.05
August		0.90
September		0.85
October		1.00
November		1.10
December		0.80

 

Total revenue for People’s Bank in 1999 is forecasted to be $60,000. Based on the seasonal indexes above, sales in the first three months of 1999 should be

800. A) $4,800.
801. B) $15,500.
802. C) $14,723.
803. D) $13,500.
804. E) None of the options are correct.

 

Answer:  B

Difficulty: 2 Medium

Topic:  The Time Series Decomposition Forecast

Learning Objective:  6-04 Explain how one determines the long-term trend for time-series decomposition.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

21) People’s Bank

 

Seasonal Indexes of sales revenue of People’s Bank are:

 

January		1.20
February		0.90
March		1.00
April		1.08
May		1.02
June		1.10
July		1.05
August		0.90
September		0.85
October		1.00
November		1.10
December		0.80

 

If December 1999 revenue for People’s Bank amounted to $5,000, a reasonable estimate of revenue for January 2000, based on the seasonal indexes given above, would be

1. A) $3,000.
2. B) $4,500.
3. C) $4,800.
4. D) $7,500.
5. E) None of the options are correct.

 

Answer:  D

Difficulty: 2 Medium

Topic:  The Time Series Decomposition Forecast

Learning Objective:  6-04 Explain how one determines the long-term trend for time-series decomposition.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

22) People’s Bank

 

Seasonal Indexes of sales revenue of People’s Bank are:

 

January		1.20
February		0.90
March		1.00
April		1.08
May		1.02
June		1.10
July		1.05
August		0.90
September		0.85
October		1.00
November		1.10
December		0.80

 

If revenue of People’s Bank amounted to $5,500 in November 1999, the November 1999 sales revenue, after adjustment for seasonal variation using the indexes given above, would be

500. A) $6,500.
501. B) $6,050.
502. C) $5,500.
503. D) $4,500.
504. E) None of the options are correct.

 

Answer:  E

Difficulty: 3 Hard

Topic:  The Time Series Decomposition Forecast

Learning Objective:  6-04 Explain how one determines the long-term trend for time-series decomposition.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

23) A company has computed a seasonal index for its quarterly sales. Which of the following statements is not correct?

4. A) The sum of the four quarterly seasonal index numbers is 4.
5. B) An index of 0.75 for quarter-one sales indicates that sales were 25 percent lower than average sales.
6. C) An index of 1.10 indicates sales 10% above the norm.
7. D) The index for any quarter must be between 0 and 2.
8. E) The average index is 1.

 

Answer:  D

Difficulty: 1 Easy

Topic:  Deseasonalizing the Data and Finding the Indices

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

24) In computing a seasonal index, specific seasonals were tabulated for each month. The averages over time for the twelve months were obtained and summed. If the mean seasonal factor for June was 96.9, and the sum for all twelve months is 1195, the adjusted seasonal index for June is

97. A) 97.7.
98. B) 96.9.
99. C) 96.4.
100. D) 102.7.
101. E) None of the options are correct.

 

Answer:  A

Difficulty: 2 Medium

Topic:  Deseasonalizing the Data and Finding the Indices

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

25) Assume the following specific seasonal factors for January are based on the ratio-to-moving average method:

 

88.2

85.9

64.3

92.4

80.1

82.4

 

What is the seasonal index for January using the modified mean method?

84. A) 84.15
85. B) 79.50
86. C) 83.34
87. D) 82.21
88. E) Not enough information is provided.

 

Answer:  E

Difficulty: 2 Medium

Topic:  Deseasonalizing the Data and Finding the Indices

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

26) The following specific seasonal factors were estimated for the month of October:

 

65.4

76.8

66.9

72.6

70.0

 

If the adjustment is 0.98 and the modified mean is used, and if the expected trend for October is $800, what is the seasonally adjusted forecast?

570. A) $570.00
571. B) $561.00
572. C) $551.20
573. D) $1,168.8
574. E) None of the options are correct.

 

Answer:  C

Difficulty: 2 Medium

Topic:  The Time Series Decomposition Forecast

Learning Objective:  6-04 Explain how one determines the long-term trend for time-series decomposition.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

27) The long-term trend of a time series in the decomposition model is estimated using

1. A) a nonlinear time trend.
2. B) the actual unsmoothed data.
3. C) the centered moving average data.
4. D) the series of seasonal factors.
5. E) All of the options are correct.

 

Answer:  C

Difficulty: 1 Easy

Topic:  Finding the Long-Term Trend

Learning Objective:  6-04 Explain how one determines the long-term trend for time-series decomposition.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

28) Consider the following data:

 

Year	Sales Revenue	Coded Time
1996	800	0
1997	840	1
1998	900	2

 

Which linear trend model best fits this data?

846. A) Y = 846.67 + 100X
847. B) Y = 840 + 100X
848. C) Y = 846.67 + 50X
849. D) Y = 796.67 + 50X
850. E) None of the options are correct.

 

Answer:  D

Difficulty: 2 Medium

Topic:  Finding the Long-Term Trend

Learning Objective:  6-04 Explain how one determines the long-term trend for time-series decomposition.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

29) Which trend model would you choose if the variable you are seeking to forecast were increasing at a constant percentage rate?

1. A) Y = a + bX
2. B) Y = abX
3. C) Y = b + b1X + b2X2
4. D) Y = b(1/X)
5. E) None of the options are correct.

 

Answer:  B

Difficulty: 1 Easy

Topic:  Finding the Long-Term Trend

Learning Objective:  6-04 Explain how one determines the long-term trend for time-series decomposition.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

30) The cyclical component of a time series is measured by

1. A) Yt/CMAt.
2. B) CMA/CMAT.
3. C) Yt/SIt.
4. D) CMAt/CMAt−1.
5. E) None of the options are correct.

 

Answer:  B

Difficulty: 1 Easy

Topic:  Measuring the Cyclical Component

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

31) The difference between cyclical and seasonal factors is best described as

1. A) they are both calculated as ratios.
2. B) amplitude.
3. C) periodicity.
4. D) wavelike random patterns.
5. E) None of the options are correct.

 

Answer:  C

Difficulty: 1 Easy

Topic:  Measuring the Cyclical Component

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

32) Which data series is not used in the calculation of cycle factors?

1. A) CMAT
2. B) CMA
3. C) TIME
4. D) SF
5. E) All of the options are correct.

 

Answer:  D

Difficulty: 1 Easy

Topic:  Measuring the Cyclical Component

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

33) A researcher mistakenly uses deseasonalized data in calculating the seasonal factors. If she found apparent seasonal behavior, this is best attributed to

1. A) the business cycle.
2. B) trend.
3. C) random noise.
4. D) seasonality.
5. E) None of the options are correct.

 

Answer:  A

Difficulty: 1 Easy

Topic:  Business Cycle Indicators

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

34) The Sky-Is-Falling forecasting firm is predicting a deep recession next year. What would be the average forecasted cycle factor for next year if you believe such a forecast?

1. A) Less than zero
2. B) Close to zero, but negative
3. C) Close to one, but greater than one
4. D) Substantially greater than one
5. E) None of the options are correct.

 

Answer:  E

Difficulty: 1 Easy

Topic:  Measuring the Cyclical Component

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

35) Which of the following is not a similarity between seasonal and cycle factors?

1. A) They are both calculated as ratios.
2. B) They both sum to the number of data points in the averaging process.
3. C) They both model variability in the dependent variable.
4. D) They both use the actual data series in their calculation.
5. E) All of the options are correct.

 

Answer:  B

Difficulty: 2 Medium

Topic:  Measuring the Cyclical Component

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

36) Which of the following is not helpful in generating forecasts of cycle factors?

1. A) A time-series plot of the data
2. B) Length of previous cycles
3. C) Amplitude of previous cycles
4. D) The prime rate of interest
5. E) All the above are helpful.

 

Answer:  E

Difficulty: 1 Easy

Topic:  Measuring the Cyclical Component

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

37) The range of economic activity from the beginning trough of an expansion to the peak of the expansion is called

1. A) the recession phase.
2. B) the contraction phase.
3. C) the expansion phase.
4. D) the periodicity.
5. E) None of the options are correct.

 

Answer:  C

Difficulty: 1 Easy

Topic:  Overview of Business Cycles

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

38) If business cycles were pure cycles, they

1. A) would have constant amplitude.
2. B) would have constant periodicity.
3. C) would be easy to forecast.
4. D) would have predictable trend reversals.
5. E) All of the options are correct.

 

Answer:  E

Difficulty: 1 Easy

Topic:  Overview of Business Cycles

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

39) Over a long period of time, if measured correctly, cycle factors should average

1. A) zero.
2. B) one.
3. C) two.
4. D) four.
5. E) twelve.

 

Answer:  B

Difficulty: 1 Easy

Topic:  Measuring the Cyclical Component

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

40) Which of the following is not part of the index of leading economic indicators?

1. A) Index of stock prices
2. B) Index of industrial production
3. C) M2 Money Supply
4. D) Index of new private housing starts
5. E) All of the options are correct.

 

Answer:  B

Difficulty: 1 Easy

Topic:  Business Cycle Indicators

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

41) Which of the following is not a part of the index of lagging economic indicators?

1. A) Average prime rate of interest
2. B) Index of unit labor costs
3. C) Outstanding commercial loans
4. D) Ratio of consumer installment credit to personal income
5. E) None of the options are correct.

 

Answer:  E

Difficulty: 1 Easy

Topic:  Business Cycle Indicators

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

42) What is the major problem when using time-series smoothing techniques to forecast the cyclical component of a time series?

1. A) It takes too much data.
2. B) It takes too much computer time and effort.
3. C) Trend reversals cannot be forecasted.
4. D) Holt’s smoothing estimates a linear trend.
5. E) All of the options are correct.

 

Answer:  C

Difficulty: 1 Easy

Topic:  Time Series Decomposition Forecast

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

43) When using moving-average smoothing to generate forecasts of cycle factors, the researcher should be wary of

1. A) spurious cycles caused by heteroscedasticity.
2. B) bias caused by heteroscedasticity.
3. C) spurious cycles caused by serial correlation.
4. D) bias in trend estimates caused by serial correlation.
5. E) All of the options are correct.

 

Answer:  C

Difficulty: 2 Medium

Topic:  Time Series Decomposition Forecast

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

44) Which of the following advice is not particularly useful in forecasting the cyclical component of a time series?

1. A) Review the past behavior of the cyclical factor series.
2. B) Use time-series smoothing when you expect a trend to continue into the forecast horizon.
3. C) Avoid subjective forecasts of cycle factors.
4. D) Review the results of several forecasting methods.
5. E) All of the options are correct.

 

Answer:  C

Difficulty: 1 Easy

Topic:  Time Series Decomposition Forecast

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

45) Which statement is not correct?

1. A) Time series decomposition tends to fit the data very well.
2. B) Time series decomposition accuracy is usually overstated by model fit statistics.
3. C) The better the forecast of the cycle factors, the better the out-of-sample fit of time-series decomposition.
4. D) Time series decomposition tends to be well understood by forecast consumers.
5. E) All of the options are correct.

 

Answer:  E

Difficulty: 1 Easy

Topic:  Time Series Decomposition Forecast

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

46) Which of the following statements regarding time series decomposition is not correct?

1. A) The fluctuating components of a time series are cyclical, seasonal, and irregular.
2. B) Short-term forecasts are more accurate than long term.
3. C) If the original data are valued in dollars, the values of the cycle factors must also be in dollars.
4. D) Seasonal index numbers for monthly data average 1 and total 12.
5. E) All of the options are correct.

 

Answer:  C

Difficulty: 1 Easy

Topic:  Time Series Decomposition Forecast

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

47) Jewelry Sales

 

Audit Trail

 

Series Description	($Millions)
Jewelry Sales	1.00

 

Audit Trail – Statistics

 

Accuracy Measures	Value		Forecast Statistics	Value
AIC	1,680.55		Durbin Watson(1)	1.54
BIC	1,686.49		Mean	1,781.55
Mean Absolute Percentage Error (MAPE)	3.15	%	Standard Deviation	1,070.01
R-Square	99.41	%	Skewness	2.85
Adjusted R-Square	99.41	%	Kurtosis	10.83
Mean Absolute Error	54.82		Max	6,554.00
Mean Error	3.01		Min	802.00
Mean Square Error	6,666.16		Mean Absolute Deviation	618.70
Root Mean Square Error	81.65		Sum Squared Deviation	163,723,589.66
Standard Deviation of Error	81.93		Mean Square Deviation	1,136,969.37
Theil	0.10		Mode	1,043.00
			Range	5,752.00
			Root Mean Square	1,066.29
			Ljng-Box	69.63

 

 

Method Statistics	Value
Method Selected	Decomposition
Basic Method	Trend (Linear) Regression
Decomposition Type	Multiplicative

 

 

Components of Decomposition

 

Date	Original Data	Forecasted Data	Centered Moving Average	CMA Trend	Seasonal Indices	Cycle Factors
Jan-1992	803.00	803.00			0.63
Feb-1992	1,030.00	1,030.00			0.89
Mar-1992	922.00	922.00			0.74
Apr-1992	977.00	977.00			0.78
May-1992	1,182.00	1,182.00			1.00
Jun-1992	1,104.00	1,104.00			0.85
Jul-1992	1,046.00	1,020.05	1,265.29	1,307.72	0.81	0.97
Aug-1992	1,100.00	1,076.55	1,264.08	1,314.85	0.85	0.96
Sep-1992	1,043.00	993.64	1,262.08	1,321.99	0.79	0.95
Oct-1992	1,132.00	1,057.19	1,262.50	1,329.12	0.84	0.95
Nov-1992	1,376.00	1,371.67	1,266.42	1,336.26	1.08	0.95
Dec-1992	3,469.00	3,512.70	1,276.00	1,343.40	2.75	0.95
Jan-1993	802.00	816.80	1,292.58	1,350.53	0.63	0.96
Feb-1993	1,002.00	1,163.70	1,309.33	1,357.67	0.89	0.96
Mar-1993	902.00	978.15	1,322.58	1,364.80	0.74	0.97
Apr-1993	1,007.00	1,038.98	1,332.13	1,371.94	0.78	0.97
May-1993	1,246.00	1,337.59	1,343.21	1,379.07	1.00	0.97
Jun-1993	1,270.00	1,154.83	1,365.92	1,386.21	0.85	0.99
Jul-1993	1,278.00	1,116.70	1,385.17	1,393.34	0.81	0.99
Aug-1993	1,270.00	1,189.99	1,397.29	1,400.48	0.85	1.00
Sep-1993	1,191.00	1,111.41	1,411.67	1,407.61	0.79	1.00
Oct-1993	1,213.00	1,193.26	1,425.00	1,414.75	0.84	1.01
Nov-1993	1,561.00	1,556.29	1,436.88	1,421.88	1.08	1.01
Dec-1993	3,829.00	3,967.96	1,441.38	1,429.02	2.75	1.01
Jan-1994	904.00	909.06	1,438.58	1,436.15	0.63	1.00
Feb-1994	1,191.00	1,278.43	1,438.42	1,443.29	0.89	1.00
Mar-1994	1,058.00	1,067.05	1,442.79	1,450.42	0.74	0.99
Apr-1994	1,171.00	1,130.66	1,449.67	1,457.56	0.78	0.99
May-1994	1,367.00	1,455.22	1,461.33	1,464.69	1.00	1.00

 

Note that this “Components” table is truncated.

 

Consider the Time Series Decomposition result above. The variable being forecasted is jewelry sales in the United States monthly. After the application of the time series decomposition model,

1. A) there appears to be no first-order serial correlation.
2. B) there appears to be first-order serial correlation.
3. C) there appears to be a problem with the underlying model as evidenced by the Theil’s-U.
4. D) there appears to be a problem with stationarity.

 

 

Answer:  A

Difficulty: 2 Medium

Topic:  Time Series Decomposition Forecast

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

48) Jewelry Sales

 

Audit Trail — Correlation Coefficient Table

 

Series Description	($Millions)
Jewelry Sales	1.00

 

Audit Trail – Statistics

 

Accuracy Measures	Value		Forecast Statistics	Value
AIC	1,680.55		Durbin Watson(1)	1.54
BIC	1,686.49		Mean	1,781.55
Mean Absolute Percentage Error (MAPE)	3.15	%	Standard Deviation	1,070.01
R-Square	99.41	%	Skewness	2.85
Adjusted R-Square	99.41	%	Kurtosis	10.83
Mean Absolute Error	54.82		Max	6,554.00
Mean Error	3.01		Min	802.00
Mean Square Error	6,666.16		Mean Absolute Deviation	618.70
Root Mean Square Error	81.65		Sum Squared Deviation	163,723,589.66
Standard Deviation of Error	81.93		Mean Square Deviation	1,136,969.37
Theil	0.10		Mode	1,043.00
			Range	5,752.00
			Root Mean Square	1,066.29
			Ljng-Box	69.63

 

 

Method Statistics	Value
Method Selected	Decomposition
Basic Method	Trend (Linear) Regression
Decomposition Type	Multiplicative

 

 

 

Components of Decomposition

 

Date	Original Data	Forecasted Data	Centered Moving Average	CMA Trend	Seasonal Indices	Cycle Factors
Jan-1992	803.00	803.00			0.63
Feb-1992	1,030.00	1,030.00			0.89
Mar-1992	922.00	922.00			0.74
Apr-1992	977.00	977.00			0.78
May-1992	1,182.00	1,182.00			1.00
Jun-1992	1,104.00	1,104.00			0.85
Jul-1992	1,046.00	1,020.05	1,265.29	1,307.72	0.81	0.97
Aug-1992	1,100.00	1,076.55	1,264.08	1,314.85	0.85	0.96
Sep-1992	1,043.00	993.64	1,262.08	1,321.99	0.79	0.95
Oct-1992	1,132.00	1,057.19	1,262.50	1,329.12	0.84	0.95
Nov-1992	1,376.00	1,371.67	1,266.42	1,336.26	1.08	0.95
Dec-1992	3,469.00	3,512.70	1,276.00	1,343.40	2.75	0.95
Jan-1993	802.00	816.80	1,292.58	1,350.53	0.63	0.96
Feb-1993	1,002.00	1,163.70	1,309.33	1,357.67	0.89	0.96
Mar-1993	902.00	978.15	1,322.58	1,364.80	0.74	0.97
Apr-1993	1,007.00	1,038.98	1,332.13	1,371.94	0.78	0.97
May-1993	1,246.00	1,337.59	1,343.21	1,379.07	1.00	0.97
Jun-1993	1,270.00	1,154.83	1,365.92	1,386.21	0.85	0.99
Jul-1993	1,278.00	1,116.70	1,385.17	1,393.34	0.81	0.99
Aug-1993	1,270.00	1,189.99	1,397.29	1,400.48	0.85	1.00
Sep-1993	1,191.00	1,111.41	1,411.67	1,407.61	0.79	1.00
Oct-1993	1,213.00	1,193.26	1,425.00	1,414.75	0.84	1.01
Nov-1993	1,561.00	1,556.29	1,436.88	1,421.88	1.08	1.01
Dec-1993	3,829.00	3,967.96	1,441.38	1,429.02	2.75	1.01
Jan-1994	904.00	909.06	1,438.58	1,436.15	0.63	1.00
Feb-1994	1,191.00	1,278.43	1,438.42	1,443.29	0.89	1.00
Mar-1994	1,058.00	1,067.05	1,442.79	1,450.42	0.74	0.99
Apr-1994	1,171.00	1,130.66	1,449.67	1,457.56	0.78	0.99
May-1994	1,367.00	1,455.22	1,461.33	1,464.69	1.00	1.00

 

Note that this “Components” table is truncated.

 

In the “Jewelry Sales” decomposition model shown above,

1. A) the cyclicality of the data seems to outweigh the seasonality in the size of the effect.
2. B) there is no trend in the original data.
3. C) there appears to be a problem with the seasonal indices as evidenced by the value of 2.75 for the month of December.
4. D) None of the options are correct.

 

 

Answer:  D

Difficulty: 2 Medium

Topic:  Time Series Decomposition Forecast

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

49) In the classical time-series decomposition model, up-and-down swings of a variable around the trend (typically lasting from one to several years each and differing in length and amplitude from one occurrence to the next) are known as

1. A) the trend component.
2. B) the cyclical component.
3. C) the seasonal component.
4. D) the irregular component.

 

Answer:  B

Difficulty: 1 Easy

Topic:  Measuring the Cyclical Component

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

50) Which of the following statements about the cyclical component of a classical time series decomposition model is false?

1. A) The cyclical component of a time series, denoted by C, is a relatively smooth, progressively upward or downward movement of a variable, Y, over an extended period of time.
2. B) The cyclical component is viewed as the consequence of long-range gradual changes in such factors as population size or composition, technology, or consumer preferences.
3. C) The cyclical component is typically computed from data that cover a minimum of 2 years.
4. D) All of the options are correct.

 

Answer:  D

Difficulty: 2 Medium

Topic:  Measuring the Cyclical Component

Learning Objective:  6-05 Describe how “cycles” in a business environment differ from true cycles.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

51) Audit Trail – Statistics

 

Accuracy Measures	Value		Forecast Statistics	Value
AIC	309.51		Durbin Watson(4)	1.01
BIC	313.82		Mean	61.54
Mean Absolute Percentage Error (MAPE)	3.11	%	Standard Deviation	12.70
R-Square	95.64	%	Variance	161.41
Adjusted R-Square	95.57	%	Ljung-Box	58.17
Root Mean Square Error	2.63
Theil	0.29

 

 

Method Statistics	Value
Method Selected	Decomposition
Basic Method	Trend (Linear) Regression
Decomposition Type	Multiplicative

 

 

 

Components of Decomposition

 

Date	Original Data	Forecasted Data	Centered Moving Average	CMA Trend	Seasonal Indices	Cycle Factors
Sep-1998	56.60				0.90
Oct-1998	49.10				1.09
Nov-1998	58.50	58.93	55.21	62.51	1.07	0.88
Dec-1998	57.50	54.10	57.63	62.45	0.94	0.92
Jan-1999	54.90	55.26	61.16	62.40	0.90	0.98
Feb-1999	70.10	66.69	61.16	62.35	1.09	0.98
Mar-1999	65.80	64.10	60.05	62.29	1.07	0.96
Apr-1999	50.20	55.93	59.58	62.24	0.94	0.96
May-1999	53.30	53.27	58.96	62.19	0.90	0.95
Jun-1999	67.90	64.62	59.26	62.13	1.09	0.95
Jul-1999	63.10	65.27	61.15	62.08	1.07	0.99
Aug-1999	55.30	60.18	64.10	62.03	0.94	1.03
Sep-1999	63.30	61.55	68.13	61.97	0.90	1.10
Oct-1999	81.50	78.71	72.19	61.92	1.09	1.17
Nov-1999	81.70	79.51	74.49	61.87	1.07	1.20
Dec-1999	69.20	70.60	75.20	61.82	0.94	1.22
Jan-2000	67.80	67.77	75.01	61.76	0.90	1.21
Feb-2000	82.70	81.01	74.30	61.71	1.09	1.20
Mar-2000	79.00	78.17	73.24	61.66	1.07	1.19
Apr-2000	66.20	67.71	72.13	61.60	0.94	1.17
May-2000	62.30	64.50	71.39	61.55	0.90	1.16
Jun-2000	79.30	77.40	70.99	61.50	1.09	1.15
Jul-2000	76.50	75.12	70.38	61.44	1.07	1.15
Aug-2000	65.50	64.11	68.29	61.39	0.94	1.11
Sep-2000	58.10	58.80	65.09	61.34	0.90	1.06
Oct-2000	66.80	67.90	62.28	61.28	1.09	1.02
Nov-2000	63.40	64.39	60.33	61.23	1.07	0.99
Dec-2000	56.10	56.10	59.05	61.18	0.94	0.97

 

Consider the time series decomposition output for Mobile Home Sales above. This decomposition model

1. A) explained about 3% of the variation in mobile home shipments.
2. B) explained about 96% of the variation in mobile home shipments.
3. C) explained about 0.27% of the variation in mobile home shipments.
4. D) None of the options are correct.

 

 

Answer:  B

Difficulty: 2 Medium

Topic:  The Basic Time Series Decomposition Model

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

52) Audit Trail – Statistics

 

Accuracy Measures	Value		Forecast Statistics	Value
AIC	309.51		Durbin Watson(4)	1.01
BIC	313.82		Mean	61.54
Mean Absolute Percentage Error (MAPE)	3.11	%	Standard Deviation	12.70
R-Square	95.64	%	Variance	161.41
Adjusted R-Square	95.57	%	Ljung-Box	58.17
Root Mean Square Error	2.63
Theil	0.29

 

Method Statistics	Value
Method Selected	Decomposition
Basic Method	Trend (Linear) Regression
Decomposition Type	Multiplicative

 

 

Components of Decomposition

 

Date	Original Data	Forecasted Data	Centered Moving Average	CMA Trend	Seasonal Indices	Cycle Factors
Sep-1998	56.60				0.90
Oct-1998	49.10				1.09
Nov-1998	58.50	58.93	55.21	62.51	1.07	0.88
Dec-1998	57.50	54.10	57.63	62.45	0.94	0.92
Jan-1999	54.90	55.26	61.16	62.40	0.90	0.98
Feb-1999	70.10	66.69	61.16	62.35	1.09	0.98
Mar-1999	65.80	64.10	60.05	62.29	1.07	0.96
Apr-1999	50.20	55.93	59.58	62.24	0.94	0.96
May-1999	53.30	53.27	58.96	62.19	0.90	0.95
Jun-1999	67.90	64.62	59.26	62.13	1.09	0.95
Jul-1999	63.10	65.27	61.15	62.08	1.07	0.99
Aug-1999	55.30	60.18	64.10	62.03	0.94	1.03
Sep-1999	63.30	61.55	68.13	61.97	0.90	1.10
Oct-1999	81.50	78.71	72.19	61.92	1.09	1.17
Nov-1999	81.70	79.51	74.49	61.87	1.07	1.20
Dec-1999	69.20	70.60	75.20	61.82	0.94	1.22
Jan-2000	67.80	67.77	75.01	61.76	0.90	1.21
Feb-2000	82.70	81.01	74.30	61.71	1.09	1.20
Mar-2000	79.00	78.17	73.24	61.66	1.07	1.19
Apr-2000	66.20	67.71	72.13	61.60	0.94	1.17
May-2000	62.30	64.50	71.39	61.55	0.90	1.16
Jun-2000	79.30	77.40	70.99	61.50	1.09	1.15
Jul-2000	76.50	75.12	70.38	61.44	1.07	1.15
Aug-2000	65.50	64.11	68.29	61.39	0.94	1.11
Sep-2000	58.10	58.80	65.09	61.34	0.90	1.06
Oct-2000	66.80	67.90	62.28	61.28	1.09	1.02
Nov-2000	63.40	64.39	60.33	61.23	1.07	0.99
Dec-2000	56.10	56.10	59.05	61.18	0.94	0.97

 

Consider the time series decomposition output for Mobile Home Sales above.

1. A) There is the possibility of 4th order serial correlation of the error terms (i.e., residuals).
2. B) There is no evidence of serial correlation of the error terms (i.e., residuals).
3. C) This model can be expected to forecast no better than a naїve model.
4. D) None of the options are correct.

 

Answer:  A

Difficulty: 2 Medium

Topic:  The Basic Time Series Decomposition Model

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

53) Audit Trail — Statistics

 

Accuracy Measures	Value		Forecast Statistics	Value
AIC	309.51		Durbin Watson(4)	1.01
BIC	313.82		Mean	61.54
Mean Absolute Percentage Error (MAPE)	3.11	%	Standard Deviation	12.70
R-Square	95.64	%	Variance	161.41
Adjusted R-Square	95.57	%	Ljung-Box	58.17
Root Mean Square Error	2.63
Theil	0.29

 

 

Method Statistics	Value
Method Selected	Decomposition
Basic Method	Trend (Linear) Regression
Decomposition Type	Multiplicative

 

 

Components of Decomposition

 

Date	Original Data	Forecasted Data	Centered Moving Average	CMA Trend	Seasonal Indices	Cycle Factors
Sep-1998	56.60				0.90
Oct-1998	49.10				1.09
Nov-1998	58.50	58.93	55.21	62.51	1.07	0.88
Dec-1998	57.50	54.10	57.63	62.45	0.94	0.92
Jan-1999	54.90	55.26	61.16	62.40	0.90	0.98
Feb-1999	70.10	66.69	61.16	62.35	1.09	0.98
Mar-1999	65.80	64.10	60.05	62.29	1.07	0.96
Apr-1999	50.20	55.93	59.58	62.24	0.94	0.96
May-1999	53.30	53.27	58.96	62.19	0.90	0.95
Jun-1999	67.90	64.62	59.26	62.13	1.09	0.95
Jul-1999	63.10	65.27	61.15	62.08	1.07	0.99
Aug-1999	55.30	60.18	64.10	62.03	0.94	1.03
Sep-1999	63.30	61.55	68.13	61.97	0.90	1.10
Oct-1999	81.50	78.71	72.19	61.92	1.09	1.17
Nov-1999	81.70	79.51	74.49	61.87	1.07	1.20
Dec-1999	69.20	70.60	75.20	61.82	0.94	1.22
Jan-2000	67.80	67.77	75.01	61.76	0.90	1.21
Feb-2000	82.70	81.01	74.30	61.71	1.09	1.20
Mar-2000	79.00	78.17	73.24	61.66	1.07	1.19
Apr-2000	66.20	67.71	72.13	61.60	0.94	1.17
May-2000	62.30	64.50	71.39	61.55	0.90	1.16
Jun-2000	79.30	77.40	70.99	61.50	1.09	1.15
Jul-2000	76.50	75.12	70.38	61.44	1.07	1.15
Aug-2000	65.50	64.11	68.29	61.39	0.94	1.11
Sep-2000	58.10	58.80	65.09	61.34	0.90	1.06
Oct-2000	66.80	67.90	62.28	61.28	1.09	1.02
Nov-2000	63.40	64.39	60.33	61.23	1.07	0.99
Dec-2000	56.10	56.10	59.05	61.18	0.94	0.97

 

Consider the time series decomposition output for Mobile Home Sales above. Mobile home shipments are modeled here

1. A) as exhibiting a flat trend.
2. B) as exhibiting a downward trend.
3. C) as exhibiting an upward trend.
4. D) None of the options are true.

 

 

Answer:  B

Difficulty: 2 Medium

Topic:  The Basic Time Series Decomposition Model

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

54) Audit Trail — Statistics

 

Accuracy Measures	Value		Forecast Statistics	Value
AIC	309.51		Durbin Watson(4)	1.01
BIC	313.82		Mean	61.54
Mean Absolute Percentage Error (MAPE)	3.11	%	Standard Deviation	12.70
R-Square	95.64	%	Variance	161.41
Adjusted R-Square	95.57	%	Ljung-Box	58.17
Root Mean Square Error	2.63
Theil	0.29

 

 

Method Statistics	Value
Method Selected	Decomposition
Basic Method	Trend (Linear) Regression
Decomposition Type	Multiplicative

 

 

Components of Decomposition

 

Date	Original Data	Forecasted Data	Centered Moving Average	CMA Trend	Seasonal Indices	Cycle Factors
Sep-1998	56.60				0.90
Oct-1998	49.10				1.09
Nov-1998	58.50	58.93	55.21	62.51	1.07	0.88
Dec-1998	57.50	54.10	57.63	62.45	0.94	0.92
Jan-1999	54.90	55.26	61.16	62.40	0.90	0.98
Feb-1999	70.10	66.69	61.16	62.35	1.09	0.98
Mar-1999	65.80	64.10	60.05	62.29	1.07	0.96
Apr-1999	50.20	55.93	59.58	62.24	0.94	0.96
May-1999	53.30	53.27	58.96	62.19	0.90	0.95
Jun-1999	67.90	64.62	59.26	62.13	1.09	0.95
Jul-1999	63.10	65.27	61.15	62.08	1.07	0.99
Aug-1999	55.30	60.18	64.10	62.03	0.94	1.03
Sep-1999	63.30	61.55	68.13	61.97	0.90	1.10
Oct-1999	81.50	78.71	72.19	61.92	1.09	1.17
Nov-1999	81.70	79.51	74.49	61.87	1.07	1.20
Dec-1999	69.20	70.60	75.20	61.82	0.94	1.22
Jan-2000	67.80	67.77	75.01	61.76	0.90	1.21
Feb-2000	82.70	81.01	74.30	61.71	1.09	1.20
Mar-2000	79.00	78.17	73.24	61.66	1.07	1.19
Apr-2000	66.20	67.71	72.13	61.60	0.94	1.17
May-2000	62.30	64.50	71.39	61.55	0.90	1.16
Jun-2000	79.30	77.40	70.99	61.50	1.09	1.15
Jul-2000	76.50	75.12	70.38	61.44	1.07	1.15
Aug-2000	65.50	64.11	68.29	61.39	0.94	1.11
Sep-2000	58.10	58.80	65.09	61.34	0.90	1.06
Oct-2000	66.80	67.90	62.28	61.28	1.09	1.02
Nov-2000	63.40	64.39	60.33	61.23	1.07	0.99
Dec-2000	56.10	56.10	59.05	61.18	0.94	0.97

 

Consider the time series decomposition output for Mobile Home Sales above. The seasonality of mobile home shipments

3. A) varies 3.11% from the average.
4. B) varies from 9% below average to 10% above the average.
5. C) varies from 10% below average to 9% above the average.
6. D) is negligible in this model.

 

 

Answer:  C

Difficulty: 2 Medium

Topic:  The Basic Time Series Decomposition Model

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic

 

 

55) Audit Trail — Statistics

 

Accuracy Measures	Value		Forecast Statistics	Value
AIC	309.51		Durbin Watson(4)	1.01
BIC	313.82		Mean	61.54
Mean Absolute Percentage Error (MAPE)	3.11	%	Standard Deviation	12.70
R-Square	95.64	%	Variance	161.41
Adjusted R-Square	95.57	%	Ljung-Box	58.17
Root Mean Square Error	2.63
Theil	0.29

 

 

Method Statistics	Value
Method Selected	Decomposition
Basic Method	Trend (Linear) Regression
Decomposition Type	Multiplicative

 

 

Components of Decomposition

 

Date	Original Data	Forecasted Data	Centered Moving Average	CMA Trend	Seasonal Indices	Cycle Factors
Sep-1998	56.60				0.90
Oct-1998	49.10				1.09
Nov-1998	58.50	58.93	55.21	62.51	1.07	0.88
Dec-1998	57.50	54.10	57.63	62.45	0.94	0.92
Jan-1999	54.90	55.26	61.16	62.40	0.90	0.98
Feb-1999	70.10	66.69	61.16	62.35	1.09	0.98
Mar-1999	65.80	64.10	60.05	62.29	1.07	0.96
Apr-1999	50.20	55.93	59.58	62.24	0.94	0.96
May-1999	53.30	53.27	58.96	62.19	0.90	0.95
Jun-1999	67.90	64.62	59.26	62.13	1.09	0.95
Jul-1999	63.10	65.27	61.15	62.08	1.07	0.99
Aug-1999	55.30	60.18	64.10	62.03	0.94	1.03
Sep-1999	63.30	61.55	68.13	61.97	0.90	1.10
Oct-1999	81.50	78.71	72.19	61.92	1.09	1.17
Nov-1999	81.70	79.51	74.49	61.87	1.07	1.20
Dec-1999	69.20	70.60	75.20	61.82	0.94	1.22
Jan-2000	67.80	67.77	75.01	61.76	0.90	1.21
Feb-2000	82.70	81.01	74.30	61.71	1.09	1.20
Mar-2000	79.00	78.17	73.24	61.66	1.07	1.19
Apr-2000	66.20	67.71	72.13	61.60	0.94	1.17
May-2000	62.30	64.50	71.39	61.55	0.90	1.16
Jun-2000	79.30	77.40	70.99	61.50	1.09	1.15
Jul-2000	76.50	75.12	70.38	61.44	1.07	1.15
Aug-2000	65.50	64.11	68.29	61.39	0.94	1.11
Sep-2000	58.10	58.80	65.09	61.34	0.90	1.06
Oct-2000	66.80	67.90	62.28	61.28	1.09	1.02
Nov-2000	63.40	64.39	60.33	61.23	1.07	0.99
Dec-2000	56.10	56.10	59.05	61.18	0.94	0.97

 

Consider the time series decomposition output for Mobile Home Sales above.

1. A) There is no effect of cycle in this model.
2. B) The effect of cycle is dramatic in this model.
3. C) The effect of cycle is not accounted for in time series decomposition.
4. D) There is no way to determine the effect of cycle from the data displayed.

 

Answer:  B

Difficulty: 2 Medium

Topic:  The Basic Time Series Decomposition Model

Learning Objective:  6-02 Explain the four components of a time series. Discuss the trend, the seasonal, and the cyclical components.

Accessibility:  Keyboard Navigation

Gradable:  automatic