Forecast methods based on judgement opinion, past experiences, or best guesses are known as
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Forecasts of commodity demand may be based on macroeconomic forecasts. Show
b. False Barometric forecasting methods are most useful for long-term forecasts.
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.
b. False Surveys and opinion polls are qualitative techniques.
b. False Qualitative forecasts based on surveys tend to perform particularly well during periods of unexpected international political upheaval.
b. False The Delphi method generates forecasts by surveying consumers to determine their opinions.
b. False One advantage of the Delphi method is that it avoids a "bandwagon effect" that could lead to incorrect or biased conclusions.
b. False Councils of distinguished foreign dignitaries and business people are used to obtain qualitative forecasts with a foreign perspective.
b. False Time-series analysis generates forecasts by identifying cause and effect relationships between variables.
b. False Time-series data are observations on a variable at different points in time.
b. False The fundamental assumption of time-series analysis is that past patterns in time-series data will continue unchanged in the future.
b. False Time-series forecasting tends to be more accurate than "naive" forecasting.
b. False The long-run increase or decrease in time-series data is referred to as a cyclical fluctuation.
b. False A time series that displays regular seasonal variation is said to exhibit cyclical fluctuation.
b. False Irregular or random influences on time-series data give rise to the secular trend.
b. False Expansions and contractions in the general economy result in seasonal variation.
b. False Cyclical fluctuations in time-series data are generally forecast using qualitative techniques.
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.
b. False The linear trend equation can be estimated by ordinary least squares regression analysis.
b. False The constant percentage growth rate model cannot be estimated by ordinary least squares regression analysis.
b. False Seasonal variation can be estimated by the use of dummy variables in linear regression analysis.
b. False The ratio-to-trend method is used to estimate a linear trend equation.
b. False A fundamental assumption of time-series analysis is that past trend and seasonal patterns will not persist in the future.
b. False Time-series analysis is particularly useful for forecasting turning points in time-series data.
b. False Naive forecasting methods include time-series analysis and smoothing methods.
b. False Smoothing techniques are most useful for time-series data that is primarily influenced by irregular variation.
b. False A moving average forecast is based on the most recent observed values of time-series data.
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.
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.
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.
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.
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.
b. False Barometric methods are often used to forecast the cyclical component of a time series.
b. False The use of leading indicators to forecast time-series data is an example of econometric forecasting.
b. False The diffusion index is a coincident indicator.
b. False The use of an estimated demand equation to forecast demand is an example of econometric forecasting.
b. False Forecasts based on leading indicators are qualitative.
b. False Macroeconomic forecasts are generally based on multiple-equation econometric models.
b. False Reduced form equations are derived algebraically from the structural and definitional equations in a multi-equation econometric model.
b. False Definitional equations must be estimated using regression analysis.
b. False What is a predictive technique that is based on judgment Opinion past experience or best guesses?Subjective probability is a type of probability derived from an individual's personal judgment or own experience about whether a specific outcome is likely to occur. It contains no formal calculations and only reflects the subject's opinions and past experience rather than on data or computation.
What are the 4 forecasting methods?While there are a wide range of frequently used quantitative budget forecasting tools, in this article we focus on the top four methods: (1) straight-line, (2) moving average, (3) simple linear regression, and (4) multiple linear regression.
Which forecasting methods are based on judgment opinion and intuition?Qualitative Methods – These methods are based on emotions, intuitions, judgments, personal experiences, and opinions. This means that there is no math involved in qualitative forecasting methods.
What are the 2 types of forecasting methods?There are two types of forecasting methods: qualitative and quantitative. Each type has different uses so it's important to pick the one that that will help you meet your goals.
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