Chapter 8

A time series that shows a recurring pattern over one year or less is said to follow a a. horizontal pattern. b. stationary pattern. c. cyclical pattern. d. seasonal pattern.

d

Autoregressive models a. use the average of the most recent data values in the time series as the forecast for the next period. b. are used to smooth out random fluctuations in time series. c. relate a time series to other variables that are believed to explain or cause its behavior. d. occur whenever all the independent variables are previous values of the time series.

d

. The value of an independent variable from the prior period is referred to as a a. lagged variable. b. dummy variable. c. predictor variable. d. categorical variable.

a

A forecast is defined as a(n) a. prediction of future values of a time series. b. quantitative method used when historical data on the variable of interest are either unavailable or not applicable. c. set of observations on a variable measured at successive points in time. d. outcome of a random experiment.

a

The mean absolute error, mean squared error, and mean absolute percentage error are all methods to measure the accuracy of a forecast. These methods measure forecast accuracy by a. determining how well a particular forecasting method is able to reproduce the time series data that are already available. b. using the current value to estimate how well the model generates previous values correctly. c. predicting the future values and wait for a pre-defined time period to examine how accurate the predictions were. d. adjusting the scale of the data.

a

The moving averages and exponential smoothing methods are appropriate for a time series exhibiting a. a horizontal pattern. b. a cyclical pattern. c. trends. d. seasonal effects.

a

Trend refers to a. the long-run shift or movement in the time series observable over several periods of time. b. the outcome of a random experiment. c. the recurring patterns observed over successive periods of time. d. the short-run shift or movement in the time series observable for some specific period of time.

a

Which is not true regarding trend patterns? a. Can result when business conditions shift to a new level at some point in time b. Exist when there are gradual shifts of values over long periods of time c. Can result from factors such as improving technology or changes in consumer preferences d. Can represent nonlinear relationships

a

A causal model provides evidence of __________ between an independent variable and the variable to be forecast. a. a causal relationship b. an association c. no relationship d. a seasonal relationship

b

A positive forecast error indicates that the forecasting method ________ the dependent variable. a. overestimated b. underestimated c. accurately estimated d. closely approximated

b

An exponential trend pattern occurs when a. the amount of increase between periods in the value of the variable is constant. b. the percentage change between periods in the value of the variable is relatively constant. c. there is a no relationship between the time series variable and time. d. there are random fluctuations in the variable value with time.

b

Demand for a product and the forecasting department's forecast (naïve model) for a product are shown below. Compute the mean squared error. Period Actual Demand Forecasted Demand 1 12 - - 2 15 12 3 14 15 4 18 16 a. 3.33 b. 4.67 c. 5.33 d. 6.67

b

For causal modeling, __________ are used to detect linear or nonlinear relationships between the independent and dependent variables. a. descriptive statistics on the data b. scatter charts c. contingency tables d. pie charts

b

The process of __________ might be used to determine the value of the smoothing constant that minimizes the mean squared error. a. quantization b. nonlinear optimization c. clustering d. curve fitting

b

Using a large value for order k in the moving averages method is effective in a. tracking changes in a time series more quickly. b. smoothing out random fluctuations. c. providing a forecast when only the most recent time series are relevant. d. eliminating the effect of seasonal variations in the time series.

b

Which of the following is not present in a time series? a. Seasonality b. Operational variations c. Trend d. Cycles

b

Which of the following is not true of a stationary time series? a. The process generating the data has a constant mean. b. The time series plot is a straight line. c. The statistical properties are independent of time. d. The variability is constant over time.

b

Which of the following measures of forecast accuracy is susceptible to the problem of positive and negative forecast errors offsetting one another? a. Mean absolute error b. Mean forecast error c. Mean squared error d. Mean absolute percentage error

b

Which of the following statements is the objective of the moving averages and exponential smoothing methods? a. To maximize forecast accuracy measures b. To smooth out random fluctuations in the time series c. To characterize the variable fluctuations by an exponential equation d. To transform a nonstationary time series into a stationary series

b

__________ uses a weighted average of past time series values as the forecast. a. The qualitative method b. Exponential smoothing c. Correlation analysis d. The causal model

b

A set of observations on a variable measured at successive points in time or over successive periods of time constitute a a. geometric series. b. time invariant set. c. time series. d. logarithmic series.

c

A time series with a seasonal pattern can be modeled by treating the season as a a. predictor variable. b. dependent variable. c. dummy variable. d. quantitative variable.

c

Demand for a product and the forecasting department's forecast (naïve model) for a product are shown below. Compute the mean absolute error. Period Actual Demand Forecasted Demand 1 12 - - 2 15 12 3 14 15 4 18 16 a. 1 b. 1.5 c. 2 d. 2.5

c

If the forecasted value of the time series variable for period 2 is 22.5 and the actual value observed for period 2 is 25, what is the forecast error in period 2? a. 3 b. 2 c. 2.5 d. -2.5

c

In the moving averages method, the order k determines the a. error tolerance. b. compensation for forecasting error. c. number of time series values under consideration. d. number of samples in each unit time period.

c

Which of the following is true of the exponential smoothing coefficient? a. It is a randomly generated value between -1 and +1. b. It is small for a time series that has relatively little random variability. c. It is chosen as the value that minimizes a selected measure of forecast accuracy such as the mean squared error. d. It is computed in relation with the order value, k, for the moving averages.

c

With reference to exponential forecasting models, a parameter that provides the weight given to the most recent time series value in the calculation of the forecast value is known as the a. moving average. b. regression coefficient. c. smoothing constant. d. mean forecast error.

c

With reference to time series data patterns, a cyclical pattern is the component of the time series that a. shows a periodic pattern lasting one year or less. b. does not vary with respect to time. c. shows a periodic pattern lasting more than one year. d. is characterized by a linear variation of the dependent variable with respect to time.

c

Causal models a. provide evidence of a causal relationship between an independent variable and the variable to be forecast. b. use the average of the most recent data values in the time series as the forecast for the next period. c. occur whenever all the independent variables are previous values of the same time series. d. relate a time series to other variables that are believed to explain or cause its behavior.

d

Forecast error a. takes a positive value when the forecast is too high. b. cannot be negative. c. cannot be zero. d. is associated with measuring forecast accuracy.

d

If a time series plot exhibits a horizontal pattern, then a. it is evident that the time series is stationary. b. the data fluctuates around the variable mean. c. there is no relationship between time and the time series variable. d. there is still not enough evidence to conclude that the time series is stationary.

d

Suppose for a particular week, the forecasted sales were $4,000. The actual sales were $3,000. What is the value of the mean absolute percentage error? a. -33.3% b. -25% c. 25% d. 33.3%

d

The exponential smoothing forecast for period t + 1 is a weighted average of the a. forecast value in period t with weight α and the actual value for period t with weight 1 - α. b. actual value in period t + 1 with weight α and the forecast for period t with weight 1 - α. c. forecast value in period t - 1 with weight α and the forecast for period t with weight 1 - α. d. actual value in period t with weight α and the forecast for period t with weight 1 - α.

d

The moving averages method refers to a forecasting method that a. is used when considerable trend, cyclical, or seasonal effects are present. b. uses regression relationship based on past time series values to predict the future time series values. c. relates a time series to other variables that are believed to explain or cause its behavior. d. uses the average of the most recent data values in the time series as the forecast for the next period.

d

Which of the following states the objective of time series analysis? a. To predict the values of a time series based on one or more other variables b. To analyze the cause-and-effect relationship of a dependent variable with a time series and one or more other variables c. To use present variable values to study what should have been the ideal past values d. To uncover a pattern in a time series and then extrapolate the pattern into the future

d

__________ is the amount by which the predicted value differs from the observed value of the time series variable. a. Mean forecast error b. Mean absolute error c. Smoothing constant d. Forecast error

d