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Types of Forecasts:

QUALITATIVE FORECASTING METHODS

Qualitative forecasting has often proven to be most effective for short-term projections. In this method of forecasting, which works best when the scope is limited, experts in the appropriate fields are asked to agree on a common forecast. Two methods are used frequently.

Delphi Method.
This method involves asking various experts what they anticipate will happen in the future relative to the subject under consideration. Experts in the automotive industry, for example, might be asked to forecast likely innovative enhancements for cars five years from now. They are not expected to be precise, but rather to provide general opinions.

Market Research Method.
This method involves surveys and questionnaires about people's subjective reactions to changes. For example, a company might develop a new way to launder clothes; after people have had an opportunity to try the new method, they would be asked for feedback about how to improve the processes or how it might be made more appealing for the general public. This method is difficult because it is hard to identify an appropriate sample that is representative of the larger audience for whom the product is intended.

QUANTITATIVE FORECASTING MODELS

Time-Series Models.
This forecasting model uses historical data to try to predict future events. For example, assume that you are interested in forecasting your monthly turnover of your business. A simple way of doing this is by conducting a so-called the moving-average forecast of your past, say, 12 months of turnover data. A more reliable method is the exponential model which allows you to estmate a few points ahead forecast. A more robust short run forecast may be obtained by conducting both techniques, allowing you to make a comparision of the two approaches. 

Time-series models provide accurate forecasts when the changes that occur in the variable's environment are slow and consistent. However, when a variable exhibits large volatalities, then any long term forecasts may prove to be unreliable. Since time-series forecasts are relatively easy and inexpensive to construct, they are used quite extensively.

Econometric Models.
Econometric models are causal models that statistically identify the relationships between variables and how changes in one or more variables cause changes in another variable. Econometric models then use the identified relationship to predict the future. Econometric models are also called regression models.

An econometric model is a way of determining the strength and statistical significance of a hypothesized relationship. These models are used extensively in economics to prove, disprove, or validate the existence of a casual relationship between two or more variables. It is obvious that this model is highly mathematical, using different statistical equations.

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