Abstract
Instead of forecasting demand for individual items separately, hierarchical forecasting is often used: total demand is forecasted for a collection of items; this total forecast then is broken down to produce the desired individual demand forecasts. To allow analytical analyses, we considered in a previous paper the simpler problem of hierarchical estimation. So from a random sample of demand periods, we estimated both the total demand for a number of items and the fraction of this total that an individual item takes; multiplying these two quantities gives the hierarchical estimate for each individual demand. From the joint distribution of the individual demands, we here present a fast and general method for finding the bias and variance of the corresponding hierarchical estimator. The method is compared with our previous results and two new applications are added.
Original language | English |
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Pages (from-to) | 303-315 |
Journal | IMA Journal of Management Mathematics |
Volume | 21 |
Issue number | 3 |
Publication status | Published - 2010 |