Deck 12: Time Series Analysis and Forecasting

Question 1

The random walk model is written as: . In this model, represents the:

A) average of the Y's
B) average of the X's
C) forecasted value
D) random series with mean 0 and some constant standard deviation

Answer 1

random series with mean 0 and some constant standard deviation

Question 2

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)

Answer 2

the autocorrelations (or a correlogram)

Question 3

The most common form of autocorrelation is positive autocorrelation, in which:

A) large observations tend to follow both large and small observations
B) small observations tend to follow both large and small observations
C) large observations tend to follow large observations and small observations tend to follow small observations
D) large observations tend to follow small observations and small observations tend to follow large observations

Answer 3

large observations tend to follow large observations and small observations tend to follow small observations

Question 4

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

Question 5

A linear trend means that the time series variable changes by a:

A) constant amount each time period
B) constant percentage each time period
C) positive amount each time period
D) negative amount each time period

Question 6

What is a component of a time series?

A) base series
B) trend
C) seasonal component
D) cyclic component
E) all of these choices

Question 7

Econometric models can also be called:

A) judgmental models
B) time series models
C) causal models
D) environmetric models

Question 8

Which summary measure 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)

Question 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 choices

Question 10

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

Question 11

In a random series, successive observations are probabilistically independent of one another. If this property is violated, the observations are said to be:

A) autocorrelated
B) intercorrelated
C) causal
D) seasonal

Question 12

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

Question 13

Which term refers to a consecutive series of observations that remain on one side of the base level?

A) outlier
B) random walk
C) run
D) variance

Question 14

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 value

Question 15

In contrast to linear trend, an exponential trend is appropriate when the time series changes by a:

A) constant amount each time period
B) constant percentage each time period
C) positive amount each time period
D) negative amount each time period

Question 16

A small p-value in the rune test provides evidence of:

A) randomness
B) nonrandomness
C) nonnormality
D) heteroscedasticity

Question 17

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

A) MAE (mean absolute error)
B) MFE (mean forecast error)
C) RMSE (root mean square error)
D) MAPE (mean absolute percentage error)

Question 18

The linear trend was estimated using a time series with 20 time periods. The forecasted value for time period 21 is:

A) 120
B) 122
C) 160
D) 162

Question 19

The runs test uses a series of 0's and 1's. The 0's and 1's typically 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

Question 20

Models such as moving averages, 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

Question 21

When using exponential smoothing, if you want the forecast to react quickly to movements in the series, you should choose:

A) values of near 1
B) values of near 0
C) values of midway between 0 and 1
D) the values based on the particular data set

Question 22

Perhaps the simplest and one of the most frequently used extrapolation methods is the:

A) moving average
B) linear trend
C) exponential trend
D) causal model

Question 23

The moving average method can also be referred to as a(n) ____ method.

A) causal
B) smoothing
C) exponential
D) econometric

Question 24

In a random walk model, the:

A) series itself is random
B) series itself is not random but its differences are random
C) series itself and its differences are random
D) series itself and its differences are not random

Question 25

A regression approach can also be used to deal with seasonality by using ____ variables for the seasons.

A) smoothing
B) response
C) residual
D) dummy

Question 26

Holt's model differs from simple exponential smoothing in that it includes a term for:

A) seasonality
B) trend
C) residuals
D) cyclical fluctuations

Question 27

The data below represents sales for a particular product. If you were to use the moving average method with a span of 3 periods, what would be your forecast for period 5?

A) 90
B) 100
C) 105
D) 110

Question 28

In a moving averages method, which of the following represent(s) the number of terms in the moving average?

A) a smoothing constant
B) the explanatory variables
C) an alpha value
D) a span

Question 29

When using exponential smoothing, a smoothing constant must be used. The value for :

A) ranges between 0 and 1
B) ranges between -1 and +1
C) equals the largest observed value in the series
D) represents the strength of the association between the forecasted and observed values

Question 30

Which of the following is not a method for dealing with seasonality in data?

A) Winter's exponential smoothing model
B) deseasonalizing the data, using any forecasting model, then reseasonalizing the data
C) multiple regression with lags for the seasons
D) multiple regression with dummy variables for the seasons

Question 31

When using Holt's model, choosing values of the smoothing constant that are near 1 will result in forecast models that:

A) react very quickly to changes in the level
B) react very quickly to changes in the trend
C) react very quickly to changes in the level and the trend
D) react very slowly to changes in the level and the trend

Question 32

A trend component of a time series is a long-term, relatively smooth pattern or direction exhibited by a series, and its duration is more than one year.

Question 33

Suppose that a simple exponential smoothing model is used (with a = 0.30) to forecast monthly sandwich sales at a local sandwich shop. After June's demand is observed at 1520 sandwiches, the forecasted demand for July is 1600 sandwiches. At the beginning of July, what would be the forecasted demand for August?

A) 1520
B) 1544
C) 1550
D) 1600

Question 34

Suppose that a simple exponential smoothing model is used (with = 0.40) to forecast monthly sandwich sales at a local sandwich shop. The forecasted demand for September was 1560 and the actual demand was 1480 sandwiches. Given this information, what would be the forecast number of sandwiches for October?

A) 1480
B) 1528
C) 1560
D) 1592

Question 35

The following are the values of a time series for the first four time periods: Using a four-period moving average, the forecasted value for time period 5 is:

A) 24.5
B) 25.5
C) 26.5
D) 27.5

Question 36

Winters' model differs from Holt's model and simple exponential smoothing in that it includes an index for:

A) seasonality
B) trend
C) residuals
D) cyclical fluctuations

Question 37

The data below represents sales for a particular product. If you were to use the moving average method with a span of 4 periods, what would be your forecast for period 5?

A) 90
B) 100
C) 105
D) 110

Question 38

A time series can consist of four different components: trend, seasonal, cyclical, and random (or noise).

Question 39

There are a variety of deseasonalizing methods, but they are typically variations of:

A) ratio-to-seasonality methods
B) ratio-to-exponential-smoothing methods
C) ratio-to-moving-average methods
D) linear trend

Question 40

A time series is any variable that is measured over time in sequential order.

Question 41

If the observations of a time series increase or decrease regularly through time, we say that the time series has a random (or noise) component.

Question 42

Econometric forecasting models, also called causal models, use regression to forecast a time series variable by using other explanatory time series variables.

Question 43

Forecasting software packages typically report several summary measures of the forecasting error. The most important of these are MAE (mean absolute error), RMSE (root mean square error), and MAPE (mean absolute percentage error).

Question 44

The time series component that reflects a wavelike pattern describing a long-term trend that is generally apparent over a number of years is called cyclical.

Question 45

A meandering pattern is an example of a random time series.

Question 46

The null hypothesis in a runs test is the data series is random.

Question 47

The time series component that reflects a long-term, relatively smooth pattern or direction exhibited by a time series over a long time period, is called seasonal.

Question 48

Assume that the trend line was calculated from quarterly data for 2011 - 2015, where t = 1 for the first quarter of 2011. The trend value for the second quarter of the year 2016 is 0.75.

Question 49

If a random series has too few runs, then it is zigzagging too often.

Question 50

Extrapolation forecasting methods are quantitative methods that use past data of a time series variable - and nothing else, except possible time itself - to forecast values of the variable.

Question 51

An exponential trend is appropriate when the time series changes by a constant percentage each period.

Question 52

The seasonal component of a time series is more difficult to predict than the cyclic component because cyclic variation is much more regular.

Question 53

A shortcoming of the RMSE (root mean square error) is that it is not in the same units as the forecast variable.

Question 54

As is the case with residuals from regression, the forecast errors for nonregression methods will always average to zero.

Question 55

The runs test is a formal test of the null hypothesis of randomness. If there are too many or too few runs in the series, then we conclude that the series is not random.

Question 56

The most common form of autocorrelation is positive autocorrelation, where large observations tend to follow large observations and small observations tend to follow small observations.

Question 57

The cyclic component of a time series is more likely to exhibit business cycles that record periods of economic recession and inflation.

Question 58

An autocorrelation is a type of correlation used to measure whether the values of a time series are related to their own past values.

Question 59

If a time series exhibits an exponential trend, then a plot of its logarithm should be approximately linear.

Question 60

You will always get more accurate forecasts by using more complex forecasting methods.

Question 61

If the span of a moving average is large - say, 12 months - then few observations go into each average, and extreme values have relatively large effect on the forecasts.

Question 62

In a random walk model, there are significantly more runs than expected, and the autocorrelations are not significant.

Question 63

Holt's method is an exponential smoothing method, which is appropriate for a series with seasonality and possibly a trend.

Question 64

The purpose of using the moving average is to take away the short-term seasonal and random variation, leaving behind a combined trend and cyclical movement.

Question 65

Seasonal variations will not be present in a deseasonalized time series.

Question 66

If we use a value close to 1 for the smoothing constant in a simple exponential smoothing model, then we expect the model to respond very slowly to changes in the level.

Question 67

The seasonal component of a time series is more likely to exhibit the relatively steady growth of a variable, such as the population of Egypt from 35 million in 1960 to 93 million in 2016.

Question 68

If we use a value close to 1 for the level smoothing constant and a value close to 0 for the trend smoothing constant in Holt's exponential smoothing model, then we expect the model to respond very quickly to changes in the level, but very slowly to changes in the trend.

Question 69

Every form of exponential smoothing model has at least one smoothing constant, which is always between 0 and 1.

Question 70

We compute the five-period moving averages for all time periods except the first two.

Question 71

Simple exponential smoothing is appropriate for a series without a pronounced trend or seasonality.

Question 72

The moving average method is perhaps the simplest and one of the most frequently-used extrapolation methods.

Question 73

In exponential smoothing models, the forecast is based on the level at time t, L_t, which is not observable and can only be estimated.

Question 74

The smoothing constants in exponential smoothing models are effectively a way to assign different weights to past levels, trends and cycles in the data.

Question 75

Correlogram is a bar chart of autocorrelation at different lags.

Question 76

A moving average is the average of the observations in the past few periods, where the number of terms in the average is the span.

Question 77

An equation for the random walk model is given by the equation: , where is the change in the time series from time t to time t - 1, is a constant, and is a random variable (noise) with mean 0 and some standard deviation .

Question 78

To deseasonalize an observation (assuming a multiplicative model of seasonality), multiply it by the appropriate seasonal index.

Question 79

To calculate the five-period moving average for a time series, we average the values in the two preceding periods, and the values in the three following time periods.

Question 80

The smoothing constant used in simple exponential smoothing is analogous to the span in moving averages.