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Trend Extrapolation


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System Dynamics Pitfalls and Pointers

Trend Extrapolation

Often datasets of interest are incomplete, with the consequence that we cannot exploit them fully in our models. Time-series data might not cover periods of interest in sufficient detail. For example, they might run out before a specific period of interest. Alternatively, we might need to focus on the relationship between two variables x and y, where beyond certain values of x, corresponding values of y may not be known. In such instances we are left to make estimates of the missing data by extending what we know, as suggested by available (measured and recorded) data.
     Extrapolation means to infer or estimate by extending or projecting known information. In mathematical terms this involves making estimates of a value of a variable outside a known range from values within a known range. To do so requires us to make certain assumptions about how the estimated values might follow logically from the known or observed values. The following is designed to explain the method of trend extrapolation and to indicate the limits of its usefulness .

 

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