Mail-Order Demands for Style Goods: Theory and Data Analysis

Abstract

A number of alternative data-generating processes are explored for mail-order demands for seasonal style-goods. Weekly demands for 126 items over an 18-week selling season provide the empirical data. Arguments are presented which result in the following candidates for data-generating processes: (1) ratios of successive forecasts are distributed lognormally; (2) ratios of successive forecasts are distributed as t (Student); (3) actual demands during unequal time periods are distributed as negative binomial. Analysis of the data suggests the negative binomial data-generating process as both most closely representing the underlying process and being simple to adapt to a decision model. The paper concludes with an example of the use of the chosen data-generating process in a decision framework, and deals briefly with some issues of implementation.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 17 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.