Question 1

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

Accepted Answer

Reconstitution is not a technique for isolating the effects of seasonality. Time series decomposition, moving averages, and regression analysis are commonly used techniques for this purpose.

Question 2

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.

Accepted Answer

This statement is true. The naïve forecasting model assumes that the future values of a time series will be the same as the most recent value. Therefore, using the number of shipments for January of the previous year as the forecast for January next year is an example of a naïve forecasting model.

Question 3

Describe smoothing techniques for forecasting models,including naive,simple average,moving average,weighted moving average,and exponential smoothing.

Accepted Answer

One group of time-series forecasting methods contains smoothing techniques.Among these techniques are naïve models,averaging techniques,and simple exponential smoothing.These techniques do much better if the time-series data are stationary or show no significant trend or seasonal effects.Naive forecasting models are models in which it is assumed that the more recent time periods of data represent the best predictions or forecasts for future outcomes.
Simple averages use the average value for some given length of previous time periods to forecast the value for the next period.
Moving averages are time period averages that are revised for each time period by including the most recent value(s)in the computation of the average and deleting the value or values that are farthest away from the present time period.A special case of the moving average is the weighted moving average,in which different weights are placed on the values from different time periods.
Simple (single)exponential smoothing is a technique in which data from previous time periods are weighted exponentially to forecast the value for the present time period.The forecaster can select how much to weight more recent values versus those of previous time periods.

Question 4

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.

Accepted Answer

Seasonal effects can cause fluctuations in data that may obscure long-term trends. Therefore, it is important to remove seasonal influences before analyzing trends using regression models.

Question 5

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

Accepted Answer

Forecast error is the difference between the observed value of the response variable and the predicted value. It is not related to the explanatory variables.

Question 6

Differentiate among various measurements of forecasting error,including mean absolute deviation and mean square error,in order to assess which forecasting method to use.

Accepted Answer

The answer of Differentiate among various measurements of forecasting error,including...

Question 7

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

Accepted Answer

This statement is true. Averaging models and exponential models are two popular categories of smoothing techniques. Averaging models smooth data by taking the average of adjacent time periods, while exponential models give more weight to more recent time periods.

Question 8

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

Accepted Answer

When the smoothing constant alpha is equal to one, the forecast for the next time period is simply equal to the actual value of the current time period, which is the same as the naïve forecasting model.

Question 9

Test for autocorrelation using the Durbin-Watson test,overcoming it by adding independent variables and transforming variables and taking advantage of it with autoregression.

Accepted Answer

The answer of Test for autocorrelation using the Durbin-Watson test,overcoming...

Question 10

Account for seasonal effects of time-series data by using decomposition and Winters' three-parameter exponential smoothing method.

Accepted Answer

The answer of Account for seasonal effects of time-series data...

Question 11

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

Accepted Answer

Linear regression models can analyze quadratic trends in time-series data by including squared terms (or other polynomial terms) of the independent variable(s) in the model, allowing for the modeling of non-linear relationships.

Question 12

Differentiate among simple index numbers,unweighted aggregate price index numbers,weighted aggregate price index numbers,Laspeyres price index numbers,and Paasche price index numbers by defining and calculating each.

Accepted Answer

The answer of Differentiate among simple index numbers,unweighted aggregate price...

Question 13

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

Accepted Answer

While exponential and logarithmic models can be used for smoothing, they are not the only categories of techniques. Other popular smoothing techniques include moving averages, Kernel smoothing, and spline models.

Question 14

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

Accepted Answer

The statement is true. The trend refers to the long-term general direction of data or a pattern that persists over time.

Question 15

Determine trend in time-series data by using linear regression trend analysis,quadratic model trend analysis,and Holt's two-parameter exponential smoothing method.

Accepted Answer

The answer of Determine trend in time-series data by using...

Question 16

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

Accepted Answer

Decomposition separates a time series into its different components, including trend, seasonality, and residual, allowing for the identification and isolation of the effects of seasonality.

Question 17

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

Accepted Answer

The answer of Mean error (ME)and mean absolute deviation (MAD)will...

Question 18

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

Accepted Answer

Naïve forecasting models can still be useful in certain situations, such as when forecasting short-term trends or when there is no reliable historical data available. However, they do not take into account trend, cyclical effects or seasonality, which can limit their accuracy and applicability in many situations.

Question 19

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

Accepted Answer

Time-series data are not gathered at just one particular point in time, but rather over a period of time at regular intervals. This allows for analysis of trends and patterns over time.

Question 20

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

Accepted Answer

A stationary time series data does not have a trend, cyclical, or seasonal effects. It's characterized by a constant mean, variance, and autocorrelation over time.

Quiz 15: Time-Series Forecasting and Index Numbers