CHAPTER 12: TIME SERIES ANALYSIS AND FORECASTING WITH ANSWERS DOWNLOAD TO SCORE A

CHAPTER 12: TIME SERIES ANALYSIS AND
FORECASTING WITH ANSWERS DOWNLOAD TO SCORE A

CHAPTER 12: Time Series Analysis and Forecasting




MULTIPLE CHOICE


1) Forecasting models can be divided into three groups. They are:
a. time series, optimization, and simulation methods
b. judgmental, extrapolation, and econometric methods
c. judgmental, random, and linear methods
d. linear, non-linear, and extrapolation methods

ANS: B PTS: 1 MSC: AACSB: Analytic | AACSB: Time Series Data


2) Extrapolation methods attempt to:
a. use non-quantitative methods to predict future values
b. search for patterns in the data and then use those to predict future values
c. find variables that are correlated with the data being predicted
d. predict the next period’s value by using the latest period’s value

ANS: B PTS: 1 MSC: AACSB: Analytic | AACSB: Time Series Data


3) Econometric models can also be called:
a. judgmental models
b. time series models
c. causal models
d. environmetric models

ANS: C PTS: 1 MSC: AACSB: Analytic | AACSB: Time Series Data


4) Models such as moving average, exponential smoothing, and linear trend use only:
a. future values of Y to forecast previous values of Y
b. previous values of Y to forecast future values of Y
c. multiple explanatory variables (not just values of Y) to forecast future values of Y
d. ratio-to-moving-average methods

, CHAPTER 12: TIME SERIES ANALYSIS AND
FORECASTING WITH ANSWERS DOWNLOAD TO SCORE A

ANS: B PTS: 1 MSC: AACSB: Analytic | AACSB: Time Series Data


5) The components of a time series include:
a. base series
b. trend
c. seasonal component
d. cyclic component
e. all of these options

ANS: E PTS: 1 MSC: AACSB: Analytic | AACSB: Time Series Data


6) The forecast error is the difference between
a. this period’s value and the next period’s value
b. the average value and the expected value of the response variable
c. the explanatory variable value and the response variable value
d. the actual value and the forecast

ANS: D PTS: 1 MSC: AACSB: Analytic | AACSB: Time Series Data


7) Which of the following is not one of the summary measures for forecast errors that is
commonly used?

, CHAPTER 12: TIME SERIES ANALYSIS AND
FORECASTING WITH ANSWERS DOWNLOAD TO SCORE A

a. MAE (mean absolute error)
b. MFE (mean forecast error)
c. RMSE (root mean square error)
d. MAPE (mean absolute percentage error)

ANS: B PTS: 1 MSC: AACSB: Analytic | AACSB: Time Series Data


8) Which of the following summary measures for forecast errors does not depend on the
units of the forecast variable?
a. MAE (mean absolute error)
b. MFE (mean forecast error)
c. RMSE (root mean square error)
d. MAPE (mean absolute percentage error)

ANS: D PTS: 1 MSC: AACSB: Analytic | AACSB: Time Series Data


9) Examples of non-random patterns that may be evident on a time series graph include:
a. trends
b. increasing variance over time
c. a meandering pattern
d. too many zigzags
e. all of these options

ANS: E PTS: 1 MSC: AACSB: Analytic | AACSB: Time Series Data


10) Which of the following is not one of the techniques that can be used to identify whether a
time series is truly random?
a. A graph (plot the data)
b. The runs test
c. A control chart
d. The autocorrelations (or a correlogram)

ANS: D PTS: 1 MSC: AACSB: Analytic | AACSB: Time Series Data


11) Related to the runs test, if you use a Z-statistic and you get a Z value greater than 2.0, this

, CHAPTER 12: TIME SERIES ANALYSIS AND
FORECASTING WITH ANSWERS DOWNLOAD TO SCORE A

means that there is evidence of in the series
a. randomness
b. nonrandomness
c. nonnormality
d. heteroscedasticity

ANS: B PTS: 1 MSC: AACSB: Analytic | AACSB: Time Series Data


12) The runs test uses a series of 0’s and 1’s. The 0’s and 1’s represent whether each observation is:
a. above or below the predicted value of Y
b. above or below the mean value of Y
c. is above or below the mean value of the previous two observations
d. is positive or negative

ANS: B PTS: 1 MSC: AACSB: Analytic | AACSB: Time Series Data


13) The idea behind the runs test is that a random number series should have a number of runs that
is:
a. large
b. small
c. not large or small
d. constant