### Abstract

Original language | English |
---|---|

Pages (from-to) | 303-315 |

Journal | IMA Journal of Management Mathematics |

Volume | 21 |

Issue number | 3 |

Publication status | Published - 2010 |

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*IMA Journal of Management Mathematics*,

*21*(3), 303-315.

}

*IMA Journal of Management Mathematics*, vol. 21, no. 3, pp. 303-315.

**Calculating the accuracy of hierarchical estimation.** / Strijbosch, L.W.G.; Moors, J.J.A.

Research output: Contribution to journal › Article › Scientific › peer-review

TY - JOUR

T1 - Calculating the accuracy of hierarchical estimation

AU - Strijbosch, L.W.G.

AU - Moors, J.J.A.

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

M3 - Article

VL - 21

SP - 303

EP - 315

JO - IMA Journal of Management Mathematics

JF - IMA Journal of Management Mathematics

SN - 1471-678X

IS - 3

ER -