Forecasts of commodity demand may be based on macroeconomic forecasts.
- a. True
b. False
Barometric forecasting methods are most useful for long-term forecasts.
- a. True
b. False
The choice of a forecasting method should be based on an assessment of the costs and benefits of each method in a specific application.
- a. True
b. False
Surveys and opinion polls are qualitative techniques.
- a. True
b. False
Qualitative forecasts based on surveys tend to perform particularly well during periods of unexpected international political upheaval.
- a. True
b. False
The Delphi method generates forecasts by surveying consumers to determine their opinions.
- a. True
b. False
One advantage of the Delphi method is that it avoids a "bandwagon effect" that could lead to incorrect or biased conclusions.
- a. True
b. False
Councils of distinguished foreign dignitaries and business people are used to obtain qualitative forecasts with a foreign perspective.
- a. True
b. False
Time-series analysis generates forecasts by identifying cause and effect relationships between variables.
- a. True
b. False
Time-series data are observations on a variable at different points in time.
- a.
True
b. False
The fundamental assumption of time-series analysis is that past patterns in time-series data will continue unchanged in the future.
- a. True
b. False
Time-series forecasting tends to be more accurate than "naive" forecasting.
- a. True
b. False
The long-run increase or decrease in time-series data is referred to as a cyclical fluctuation.
- a. True
b. False
A time series that displays regular seasonal variation is said to exhibit cyclical fluctuation.
- a. True
b. False
Irregular or random influences on time-series data give rise to the secular trend.
- a.
True
b. False
Expansions and contractions in the general economy result in seasonal variation.
- a. True
b. False
Cyclical fluctuations in time-series data are generally forecast using qualitative techniques.
- a. True
b. False
The use of a linear trend equation to forecast future values of a variable is based on the assumption of a constant amount of change per time period.
- a. True
b. False
The linear trend equation can be estimated by ordinary least squares regression analysis.
- a. True
b. False
The constant percentage growth rate model cannot be estimated by ordinary least squares regression analysis.
- a. True
b. False
Seasonal variation can be estimated by the use of dummy variables in linear regression analysis.
- a. True
b. False
The ratio-to-trend method is used to estimate a linear trend equation.
- a. True
b. False
A fundamental assumption of time-series analysis is that past trend and seasonal patterns will not persist in the future.
- a. True
b. False
Time-series analysis is particularly useful for forecasting turning points in time-series data.
- a. True
b. False
Naive forecasting methods include time-series analysis and smoothing methods.
- a. True
b. False
Smoothing techniques are most useful for time-series data that is primarily influenced by irregular variation.
- a. True
b. False
A moving average forecast is based on the most recent observed values of time-series data.
- a. True
b. False
The greater the number of periods used to calculate a moving average, the more sensitive the forecast is to the most recent observation.
- a. True
b. False
In general, the greater the degree of irregular or random variation present in a time series, the more periods should be used to calculate a moving average forecast.
- a. True
b. False
If two forecasting methods are applied to the same data set, the method that yields the larger root-mean-square error [RMSE] is better.
- a. True
b. False
A forecast calculated using the exponential smoothing method is a weighted average of past observations in which the most recent observation has the greatest weight.
- a. True
b. False
The weight [w] that is used to calculate an exponential smoothing forecast defines the contribution of the most recent observation to the forecast.
- a. True
b. False
Barometric methods are often used to forecast the cyclical component of a time series.
- a. True
b. False
The use of leading indicators to forecast time-series data is an example of econometric forecasting.
- a. True
b. False
The diffusion index is a coincident indicator.
- a. True
b. False
The use of an estimated demand equation to forecast demand is an example of econometric forecasting.
- a. True
b. False
Forecasts based on leading indicators are qualitative.
- a. True
b. False
Macroeconomic forecasts are generally based on multiple-equation econometric models.
- a. True
b. False
Reduced form equations are derived algebraically from the structural and definitional equations in a multi-equation econometric model.
- a. True
b. False
Definitional equations must be estimated using regression analysis.
- a. True
b. False