Chapter 6 True or False Quiz

. Forecasts of commodity demand may be based on macroeconomic forecasts.

. Barometric forecasting methods are most useful for long-term forecasts.

. The choice of a forecasting method should be based on an assessment of the costs and benefits of each method in a specific application.

. Surveys and opinion polls are qualitative techniques.

. Qualitative forecasts based on surveys tend to perform particularly well during periods of unexpected international political upheaval.

. The Delphi method generates forecasts by surveying consumers to determine their opinions.

. One advantage of the Delphi method is that it avoids a bandwagon effect" that could lead to incorrect or biased conclusions. "

. Councils of distinguished foreign dignitaries and business people are used to obtain qualitative forecasts with a foreign perspective.

. Time-series analysis generates forecasts by identifying cause and effect relationships between variables.

. Time-series data are observations on a variable at different points in time.

. The fundamental assumption of time-series analysis is that past patterns in time-series data will continue unchanged in the future.

. Time-series forecasting tends to be more accurate than naive" forecasting. "

. The long-run increase or decrease in time-series data is referred to as a cyclical fluctuation.

. A time series that displays regular seasonal variation is said to exhibit cyclical fluctuation.

. Irregular or random influences on time-series data give rise to the secular trend.

. Expansions and contractions in the general economy result in seasonal variation.

. Cyclical fluctuations in time-series data are generally forecast using qualitative techniques.

. 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.

. The linear trend equation can be estimated by ordinary least squares regression analysis.

. The constant percentage growth rate model cannot be estimated by ordinary least squares regression analysis.

. Seasonal variation can be estimated by the use of dummy variables in linear regression analysis.

. The ratio-to-trend method is used to estimate a linear trend equation.

. A fundamental assumption of time-series analysis is that past trend and seasonal patterns will not persist in the future.

. Time-series analysis is particularly useful for forecasting turning points in time-series data.

. Naive forecasting methods include time-series analysis and smoothing methods.

. Smoothing techniques are most useful for time-series data that is primarily influenced by irregular variation.

. A moving average forecast is based on the most recent observed values of time-series data.

. The greater the number of periods used to calculate a moving average, the more sensitive the forecast is to the most recent observation.

. 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.

. 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 forecast calculated using the exponential smoothing method is a weighted average of past observations in which the most recent observation has the greatest weight.

. The weight (w) that is used to calculate an exponential smoothing forecast defines the contribution of the most recent observation to the forecast.

. Barometric methods are often used to forecast the cyclical component of a time series.

. The use of leading indicators to forecast time-series data is an example of econometric forecasting.

. The diffusion index is a coincident indicator.

. The use of an estimated demand equation to forecast demand is an example of econometric forecasting.

. Forecasts based on leading indicators are qualitative.

. Macroeconomic forecasts are generally based on multiple-equation econometric models.

. Reduced form equations are derived algebraically from the structural and definitional equations in a multi-equation econometric model.

. Definitional equations must be estimated using regression analysis.

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