Deck 15: Time-Series Forecasting and Index Numbers

Question 1

Forecast error is the difference between the value of the response variable and those of the explanatory variables.

Answer 1

False

Question 2

Two popular general categories of smoothing techniques are exponential models and logarithmic models.

Answer 2

False

Question 3

If autocorrelation occurs in regression analysis, then the confidence intervals and tests using the t and F distributions are no longer strictly applicable.

Answer 3

True

Question 4

Naïve forecasting models have no useful applications because they do not take into account data trend, cyclical effects or seasonality.

Question 5

Time-series data are data gathered on a desired characteristic at a particular point in time.

Question 6

When the error terms of a regression forecasting model are correlated the problem of multicollinearity occurs.

Question 7

Autocorrelation in a regression forecasting model can be detected by the F test.

Question 8

One of the main techniques for isolating the effects of seasonality is reconstitution.

Question 9

Autoregression is a multiple regression technique in which the independent variables are time-lagged versions of the dependent variable.

Question 10

One of the ways to overcome the autocorrelation problem in a regression forecasting model is to increase the level of significance for the F test

Question 11

Mean error (ME)and mean absolute deviation (MAD)will have the same numerical value if all errors are positive.

Question 12

An exponential smoothing technique in which the smoothing constant alpha is equal to one is equivalent to a naïve forecasting model.

Question 13

Because seasonal effects can confound trend analysis, it is important to make sure that the data is free of seasonality prior to using regression models to analyze trend.

Question 14

One of the main techniques for isolating the effects of seasonality is decomposition.

Question 15

The long-term general direction of data is referred to as trend.

Question 16

Two popular general categories of smoothing techniques are averaging models and exponential models.

Question 17

A stationary time-series data has only trend but no cyclical or seasonal effects.

Question 18

One of the ways to overcome the autocorrelation problem in a regression forecasting model is to transform the variables by taking the first-order differences.

Question 19

When a trucking firm uses the number of shipments for January of the previous year as the forecast for January next year, it is using a naïve forecasting model.

Question 20

Linear regression models cannot be used to analyze quadratic trends in time-series data.

Question 21

A time series with forecast values and error terms is presented in the following table.The mean error (ME)for this forecast is ___.

A)-0.80
B)-1.00
C)-4.00
D)8.00
E)1.00

Question 22

A time series with forecast values is presented in the following table: On this table, a is some nondisclosed value.The mean square error (MSE)is ______% of a.

A)4.15
B)0.415
C)4.15a
D)0.415a
E)0.00332

Question 23

What is the forecast for the Period 7 using a 3-period moving average technique, given the following time-series data for six past periods?

A)164.67
B)156.00
C)148.00
D)126.57
E)158.67

Question 24

A time series with forecast values is presented in the following table: On this table, a is some nondisclosed value.The mean absolute deviation (MAD)
Is ______% of a.

A)6a
B)0.06a
C)6
D)0.06
E)4.8

Question 25

A time series with forecast values and error terms is presented in the following table.The mean absolute deviation (MAD)for this forecast is ___.

A)3.10
B)12.40
C)2.48
D)6.67
E)5.10

Question 26

A time series with forecast values and error terms is presented in the following table.The mean error (ME)for this forecast is ___.

A)1.67
B)1.34
C)6.68
D)3.67
E)2.87

Question 27

A time series with forecast values and error terms is presented in the following table.The mean squared error (MSE)for this forecast is ___.

A)13.33
B)17.94
C)89.71
D)22.42
E)32.34

Question 28

A time series with forecast values is presented in the following table: If the mean absolute deviation (MAD)is 257, then a = ______.

A)4283.33
B)428.33
C)15.42
D)42.833
E)1.542

Question 29

Use of a smoothing constant value less than 0.5 in an exponential smoothing model gives more weight to ___.

A)the actual value for the current period
B)the actual value for the previous period
C)the forecast for the current period
D)the forecast for the previous period
E)the forecast for the next period

Question 30

The forecast value for September was 10.6 and the actual value turned out to be 7.Using exponential smoothing with $\alpha$ = 0.20, the forecast value for October would be ___.

A)10.10
B)9.88
C)12.00
D)10.6
E)8.88

Question 31

In exponential smoothing models, the value of the smoothing constant may be any number between ___.

A)-1 and 1
B)-5 and 5
C)0 and 1
D)0 and 10
E)0 and 100

Question 32

When forecasting with exponential smoothing, data from previous periods is ___.

A)given equal importance
B)given exponentially increasing importance
C)ignored
D)given exponentially decreasing importance
E)linearly decreasing importance

Question 33

Using a three-month moving average (with weights of 5, 3, and 1 for the most current value, next most current value and oldest value, respectively), the forecast value for November in the following time series would be ___.

A)7.67
B)8
C)9
D)6.89
E)11

Question 34

Use of a smoothing constant value greater than 0.5 in an exponential smoothing model gives more weight to ___.

A)the actual value for the current period
B)the actual value for the previous period
C)the forecast for the current period
D)the forecast for the previous period
E)the forecast for the next period

Question 35

Using a three-month moving average, the forecast value for November in the following time series would be ___.

A)7.67
B)8
C)9
D)6.89
E)11.00

Question 36

Using a three-month moving average (with weights of 5, 3, and 1 for the most current value, next most current value and oldest value, respectively), the forecast value for October made at the end of September in the following time series would be ___.

A)7.67
B)8
C)9
D)6.89
E)11

Question 37

Using a three-month moving average, the forecast value for October made at the end of September in the following time series would be ___.

A)7.67
B)8
C)9
D)6.89
E)7.25

Question 38

A time series with forecast values and error terms is presented in the following table.The mean squared error (MSE)for this forecast is ___.

A)8.86
B)44.31
C)3.28
D)11.08
E)28.01

Question 39

A time series with forecast values and error terms is presented in the following table.The mean absolute deviation (MAD)for this forecast is ___.

A)3.54
B)7.41
C)4.43
D)17.72
E)4.34

Question 40

The forecast value for August was 12 and the actual value turned out to be 5.Using exponential smoothing with $\alpha$ = 0.20, the forecast value for September would be ___.

A)10.10
B)9.88
C)12.00
D)10.6
E)11

Question 41

In an autoregressive forecasting model, the independent variable(s)is (are)___.

A)time-lagged values of the dependent variable
B)first-order differences of the dependent variable
C)second-order, or higher, differences of the dependent variable
D)first-order quotients of the dependent variable
E)time-lagged values of the independent variable

Question 42

Analysis of data for an autoregressive forecasting model produced the following tables: The actual values of this time series, y, were 228, 54, and 191 for May, June, and July, respectively.The predicted (forecast)value for August is ___.

A)174.41
B)83.67
C)218.71
D)36.91
E)191

Question 43

The high and low values of the "ratios of actuals to moving average" are ignored when finalizing the seasonal index for a period (month or quarter)in time series decomposition.The rationale for this is to ___.

A)reduce the sample size
B)eliminate autocorrelation
C)minimize serial correlation
D)eliminate the irregular component
E)eliminate the trend

Question 44

Which of the following is not a component of time series data?

A)trend
B)seasonal fluctuations
C)cyclical fluctuations
D)normal fluctuations
E)irregular fluctuations

Question 45

Using a three-month moving average, the forecast value for October made at the end of September in the following time series would be ___.

A)11.60
B)10.00
C)9.07
D)8.06
E)9.67

Question 46

The following graph of a time-series data suggests a ___ trend.

A)linear
B)quadratic
C)cosine
D)tangential
E)flat

Question 47

The ratios of "actuals to moving averages" (seasonal indexes)for a time series are presented in the following table as percentages: The final (completely adjusted)estimate of the seasonal index for Q₁ is ___.

A)109.733
B)109.921
C)113.853
D)113.492
E)111.545

Question 48

Using a three-month moving average (with weights of 6, 3, and 1 for the most current value, next most current value and oldest value, respectively), the forecast value for November in the following time series is ___.

A)11.60
B)10.00
C)9.67
D)8.06
E)8.60

Question 49

The following graph of time-series data suggests a ___ trend.

A)quadratic
B)cosine
C)linear
D)tangential
E)flat

Question 50

Fitting a linear trend to 36 monthly data points (January 2017 = 1, February 2017 = 2, March 2017 = 3, etc.)produced the following tables: The projected trend value for January 2020 is ___.

A)544.29
B)868.61
C)652.39
D)760.50
E)876.90

Question 51

Using a three-month moving average (with weights of 6, 3, and 1 for the most current value, next most current value and oldest value, respectively), the forecast value for October made at the end of September in the following time series would be___.

A)11.60
B)10.00
C)9.67
D)8.60
E)6.11

Question 52

Fitting a linear trend to 36 monthly data points (January 2017 = 1, February 2017 = 2, March 2017 = 3, etc.)produced the following tables: The projected trend value for January 2020 is ___.

A)231.39
B)555.71
C)339.50
D)447.76
E)355.71

Question 53

The following graph of time-series data suggests a ___ trend.

A)linear
B)quadratic
C)cosine
D)tangential
E)flat

Question 54

Analysis of data for an autoregressive forecasting model produced the following tables: The actual values of this time series, y, were 228, 54, and 191 for May, June, and July, respectively.The forecast value predicted by the model for July is ___.

A)36.91
B)83.67
C)218.71
D)174.41
E)191

Question 55

Analysis of data for an autoregressive forecasting model produced the following tables: The forecasting model is ___.

A)y_t = 5.745787 + 0.062849 y_t-1 + 0.065709 y_t-2
B)y_t = 4.85094 - 0.10434 y_t-1 + 0.962669 y_t-2
C)y_t = 0.84426 - 1.66023 y_t-1 + 14.65023 y_t-2
D)y_t = 0.40299 + 0.103822 y_t-1 + 9.y_t-2
E)y_t = 0.40299 + 0.103822 y_t-1 - 9.y_t-2

Question 56

The ratios of "actuals to moving averages" (seasonal indexes)for a time series are presented in the following table as percentages: The initial estimate of the seasonal index for Q₁ is ___.

A)111.047
B)111.741
C)111.523
D)111.243
E)111.943

Question 57

Analysis of data for an autoregressive forecasting model produced the following tables: The results indicate that ___.

A)the first predictor, y_t-1, is significant at the 5% level
B)the second predictor, y_t-2, is significant at the 5% level
C)all predictor variables are significant at the 5% level
D)none of the predictor variables are significant at the 5% level
E)the overall regression model is not significant at 5% level

Question 58

Calculating the "ratios of actuals to moving average" is a common step in time series decomposition.The results (the quotients)of this step estimate the ___.

A)trend and cyclical components
B)seasonal and irregular components
C)cyclical and irregular components
D)trend and seasonal components
E)irregular components

Question 59

The following graph of a time-series data suggests a ___ trend.

A)linear
B)tangential
C)cosine
D)quadratic
E)flat

Question 60

Using a three-month moving average, the forecast value for November in the following time series is ___.

A)11.60
B)10.00
C)9.67
D)8.60
E)6.00

Question 61

Using 2019 as the base year, the 2018 value of the Laspeyres Price Index is ___.

A)69.92
B)144.06
C)100.21
D)79.72
E)99.72

Question 62

Weighted aggregate price indexes are also known as ___.

A)unbalanced indexes
B)balanced indexes
C)value indexes
D)multiplicative indexes
E)overall indexes

Question 63

Using 2016 as the base year, the 2018 value of a simple price index for the following price data is ___.

A)77.60
B)114.13
C)160.58
D)99.30
E)100.00

Question 64

When constructing a weighted aggregate price index, the weights usually are ___.

A)prices of substitute items
B)prices of complementary items
C)quantities of the respective items
D)squared quantities of the respective items
E)quality of individual items

Question 65

Jim Royo, manager of Billings Building Supply (BBS), wants to develop a model to forecast BBS's monthly sales (in $1,000's).He selects the dollar value of residential building permits (in $10,000)as the predictor variable.An analysis of the data yielded the following tables: Using $\alpha$ = 0.05 the critical value of the Durbin-Watson statistic, d_L, is ___.

A)1.24
B)1.22
C)1.13
D)1.15
E)1.85

Question 66

Jim Royo, manager of Billings Building Supply (BBS), wants to develop a model to forecast BBS's monthly sales (in $1,000's).He selects the dollar value of residential building permits (in $10,000)as the predictor variable.An analysis of the data yielded the following tables: Jim's calculated value for the Durbin-Watson statistic is 1.93.Using $\alpha$ = 0.05, the appropriate decision is: ___.

A)do not reject H₀: $\rho$ = 0
B)reject H₀: $\rho$ ≠ 0
C)do not reject: $\rho$ $\neq$ 0
D)the test is inconclusive
E)reject H₀: $\rho$ = 0

Question 67

Often, index numbers are expressed as ___.

A)percentages
B)frequencies
C)cycles
D)regression coefficients
E)correlation coefficients

Question 68

The motivation for using an index number is to ___.

A)transform the data to a standard normal distribution
B)transform the data for a linear model
C)eliminate bias from the sample
D)reduce data to an easier-to-use, more convenient form
E)reduce the variance in the data

Question 69

Jim Royo, manager of Billings Building Supply (BBS), wants to develop a model to forecast BBS's monthly sales (in $1,000's).He selects the dollar value of residential building permits (in $10,000)as the predictor variable.An analysis of the data yielded the following tables: Jim's calculated value for the Durbin-Watson statistic is 1.14.Using $\alpha$ = 0.05, the appropriate decision is: ___.

A)do not reject H₀: $\rho$ = 0
B)reject H₀: $\rho$ = 0
C)do not reject H₀: $\rho$$\neq$ 0
D)the test is inconclusive
E)reject H₀: $\rho$ ≠ 0

Question 70

Typically, the denominator used to calculate an index number is a measurement for the ___ period.

A)base
B)current
C)spanning
D)intermediate
E)peak

Question 71

Using 2019 as the base year, the 2018 value of the Paasche Price Index is ___.(Quantities are averages for the student body.)

A)80.72
B)162.28
C)240.06
D)50.45
E)30.35

Question 72

Using 2019 as the base year, the 2018 value of the Paasche Price Index is ___.

A)99.79
B)192.51
C)100.29
D)59.19
E)39.99

Question 73

Jim Royo, manager of Billings Building Supply (BBS), wants to develop a model to forecast BBS's monthly sales (in $1,000's).He selects the dollar value of residential building permits (in $10,000)as the predictor variable.An analysis of the data yielded the following tables: Using $\alpha$ = 0.05 the critical value of the Durbin-Watson statistic, d_U, is ___.

A)1.54
B)1.42
C)1.43
D)1.44
E)1.85

Question 74

Index numbers facilitate comparison of ___.

A)means
B)data over time
C)variances
D)samples
E)deviations