QMB Exam 4

Time series decomposition can be used to separate or decompose a time series into all of the following components except:

Which of the following exponential smoothing constant values puts the same weight on the most recent time series value as does a 5-period moving average?

The method that uses the average of the most recent k data values in the time series as the forecast for the next period is called:

What kind of model is appropriate if the seasonal fluctuations change over time, growing larger as the dependent variable increases because of a long-term linear trend?

A forecast model of the form Tt=b0+b1t+b2t^2 is called a(n):

Which of the following is not present in a time series?

When historical data on the variable being forecast are either not applicable or unavailable, what kind of forecasting method should be used?

What forecasting method involves selecting a different weight for the most recent k data values in the time series and then computing a weighted average of the values?

In the linear trend equation, Tt = b0 + b1t, b0 represents the:

If the historical data are restricted to past values of the variable to be forecast, the forecasting procedure is called a:

Suppose sales data for the first quarter had a seasonal index of .80. Which of the following statements correctly interprets this figure?

To model a time series with a seasonal pattern, we treat the season as a(n):

When using a categorical variable in a multiple regression model that has k levels, how many dummy variables are needed?

What forecasting method uses a weighted average of past time series values as the forecast; it is a special case of the weighted moving averages method in which we select only one weight—the weight for the most recent observation?

When using a weighted moving average, if we believe that the recent past is a better predictor of the future than the distant past, we should:

A seasonal pattern

A time series from which the effect of season has been removed by dividing each original time series observation by the corresponding seasonal index is called a:

A positive forecast error indicates that the forecasting method _____ the dependent variable.

What kind of forecasting method is based on the assumption that the variable we are forecasting has a cause-effect relationship with one or more other variables?

The average of the sum of squared forecast errors is called:

Seasonal and irregular components with values greater than 1.00 indicate effects _____ the trend estimate and values below 1.00 indicate effects _____ the trend estimate. Which two words correctly complete this sentence?

The time series model that is appropriate in situations where the seasonal fluctuations do not depend upon the level of the time series is:

The time series component that reflects gradual variability over a long time period is called:

The difference between the actual time series value and the forecast is called:

A time series method that is used to separate or decompose a time series into seasonal, trend, and irregular components is called:

What is the component of a time series model that is attributable to multiyear cycles in the time series?

The average of the absolute values of the forecast errors is called:

The average of all the historical data will always provide the best results as long as the underlying time series is:

If data for a time series analysis is collected on an annual basis only, which component may be ignored?

Time series regression refers to the use of regression analysis when the independent variable is:

Three of the following forecasting methods are appropriate for a time series with a horizontal pattern. Which one is not appropriate for a time series with a horizontal pattern?

Which of the following set of circumstances is not needed to carry out quantitative forecasting methods?

What type of analysis aims to discover a pattern in the historical data or time series and then extrapolate the pattern into the future?

All of the following are true about qualitative forecasting methods except: