Question 1

In general,the number of dummy variables used to model constant seasonal variation is equal to the number of:

Accepted Answer

The number of dummy variables used to model constant seasonal variation is equal to the number of seasons minus 1. This is because if we have n seasons, we only need n-1 dummy variables to capture the seasonal variation, as the nth season can be captured by the intercept or constant term in the model.

Question 2

When the magnitude of the seasonal swing does not depend on the level of a time series,we call this _________ variation.

Accepted Answer

Constant seasonal variation means that the magnitude of the seasonal swing remains the same regardless of the level of the time series.

Question 3

Since a(n)____________ index employs the base-period quantities in all succeeding periods,it allows for ready comparisons for identical quantities of goods purchased between the base period and all succeeding periods.

Accepted Answer

Laspeyres index measures the change in the quantity of goods purchased in successive periods using the base period prices. It is a fixed-base index and is suitable for comparing the prices of the identical quantities purchased in the base period and all succeeding periods. Therefore, Laspeyres index is the best choice for the given scenario.

Question 4

A sustained long-term change in the level of the variable that is being forecasted per unit of time is:

Accepted Answer

A sustained long-term change in the level of the variable that is being forecasted per unit of time is known as a trend. Trends reflect changes in the underlying factors that drive the variable being forecasted, and they can have a significant impact on future forecasts if not properly accounted for.

Question 5

The ___________ component of a time series reflects the long-run decline or growth in a time series.

Accepted Answer

The trend component of a time series reflects the long-run decline or growth in a time series. This component can be depicted using methods such as moving averages or regression models.

Question 6

Assume that the current date is February 1,2003.The linear regression model was applied to a monthly time series based on the last 24 months' sales (from January 2000 through December 2002).The following partial computer output summarizes the results. $$\begin{array} { l c c } 
\text { Coefficient } & \text { Estimate } & \mathrm { t } \
\text { Intercept } & 4.3 & 2.07 \
\text { Slope } & 1.6 & 2.98
\end{array}$$
Determine the predicted sales for this month.

Accepted Answer

The answer of Assume that the current date is February...

Question 7

Which of the following time series forecasting methods would not be used to forecast seasonal data?

Accepted Answer

Simple exponential smoothing is not suitable for forecasting seasonal data. It is better suited for forecasting data with no clear patterns or seasonality. Dummy variable regression, time series decomposition, and multiplicative Winters method are all appropriate methods for forecasting seasonal data.

Question 8

When the moving average method is used to estimate the seasonal factors with quarterly sales data,a ______ period moving average is used.

Accepted Answer

When using quarterly data, seasonal factors are typically estimated using a 4-period moving average. This means that the seasonal factor for each quarter is calculated as the average of that quarter's sales over the past 4 years. Using a longer or shorter moving average can result in over-smoothing or under-smoothing the seasonal pattern, respectively. Therefore, a 4-period moving average is the best choice for estimating seasonal factors with quarterly sales data.

Question 9

A restaurant has been experiencing higher sales during the weekends,compared to the weekdays.Daily restaurant sales patterns for this restaurant over a week are an example of a(n)_________ component of a time series.

Accepted Answer

The higher sales during weekends indicate a repetitive pattern that occurs within a specific time period, which is a characteristic of seasonal components in a time series. Trends refer to long-term patterns in the data, cyclical components refer to recurring but non-repeating patterns, and irregular components are unpredictable fluctuations.

Question 10

In the multiplicative decomposition method,the centered moving averages provide an estimate of:

Accepted Answer

The centered moving averages in the multiplicative decomposition method provide an estimate of the trend-cycle component, which is the combination of trend and cycle.

Question 11

A major drawback of the aggregate price index is that:

Accepted Answer

The major drawback of the aggregate price index is that it does not take into account the fact that some items in the market basket are purchased more frequently than others. This can cause the index to be biased towards items that are purchased less frequently and may not be reflective of actual consumer spending patterns.

Question 12

Suppose that the unadjusted seasonal factor for the month of April is 1.10.The sum of the 12 months' unadjusted seasonal factor values is 12.18.The normalized (adjusted)seasonal factor value for April:

Accepted Answer

The normalized seasonal factors are obtained by dividing each unadjusted seasonal factor by their average. Since the sum of all unadjusted seasonal factors is 12.18, their average value is 12.18/12 = 1.015. Therefore, the normalized seasonal factor for April would be 1.10/1.015 = 1.084, which is smaller than 1.10. Thus, the correct answer is B.

Question 13

The ___________ component of a time series refers to the erratic time series movements that follow no recognizable or regular pattern.

Accepted Answer

The irregular component of a time series refers to the random and unpredictable variations that cannot be attributed to any identifiable pattern or trend.

Question 14

Seasonal variations are periodic patterns in a time series that are repeated over time.For which one of the following time series variables is it not possible to recognize seasonal variations?

Accepted Answer

Seasonal variations involve patterns that repeat within a year or a shorter period. Years are much longer time intervals, and therefore it is difficult to recognize patterns that repeat within a year for yearly data. Therefore, it is not possible to recognize seasonal variations in a time series variable representing years.

Question 15

A sequence of values of some variable or composite of variables taken at successive,uninterrupted time periods,is called a:

Accepted Answer

A time series refers to a sequence of values of some variable taken at successive, uninterrupted time periods. The other options are specific techniques or components that can be used to analyze or model time series data.

Question 16

Assume that the current date is February 1,2003.The linear regression model was applied to a monthly time series based on the last 24 months' sales (from January 2000 through December 2002).The following partial computer output summarizes the results. $$\begin{array} { l c c } 
\text { Coefficient } & \text { Estimate } & \mathrm { t } \
\text { Intercept } & 4.3 & 2.07 \
\text { Slope } & 1.6 & 2.98
\end{array}$$
At a significance level of .05,what is the value of the rejection point in testing the slope for significance?

Accepted Answer

The rejection point for the slope is determined by the t-distribution with n-2 degrees of freedom. With 24 months of data, n = 24, so degrees of freedom = 22. At a significance level of 0.05 for a two-tailed test, the critical t-value is approximately 2.074.

Question 17

Which of the following time series forecasting methods would not be used to forecast a time series that exhibits a linear trend with no seasonal or cyclical patterns?

Accepted Answer

Dummy variable regression and Multiplicative Winters method are both used to forecast time series with seasonal or cyclical patterns.

Question 18

The ___________ component of a time series consists of erratic and unsystematic fluctuations in the time series data.

Accepted Answer

The "Irregular" component refers to the random and unpredictable fluctuations in a time series that cannot be attributed to any systematic factor like trend, seasonality, or cyclical patterns. These irregular fluctuations are often caused by unpredictable events or factors like natural disasters, economic shocks, or human error.

Question 19

The ________ component of a time series measures the fluctuations in a time series due to economic conditions of prosperity and recession with a duration of approximately 2 years or longer.

Accepted Answer

The cyclical component of a time series reflects changes that occur due to the state of the economy, such as booms or recessions, with a duration of approximately 2 years or longer. The trend component generally represents long-term growth or decline in a time series, while the seasonal component captures repetitive patterns that occur within a year. The irregular component represents random fluctuations or noise that cannot be explained by any of the other components.

Question 20

Those fluctuations that are associated with climate,holidays,and related activities are referred to as __________ variations.

Accepted Answer

Fluctuations associated with climate and holidays follow a regular pattern, hence they are referred to as seasonal variations. Trends are long-term changes, cyclical variations occur at regular intervals, and irregular variations have no predictable pattern.

Quiz 16: Time Series Forecasting and Index Numbers