A quality-based approach to estimating quantitative elasticities for differentiated products: an application to retail milk demand

A quality-based approach to estimating quantitative elasticities for differentiated products: an... This article introduces the Hedonic Metric (HM) approach as an original method to model the demand for differentiated products using qualitative attributes. Our approach is based on a two-stage estimation procedure that utilizes qualitative characteristics of products. First, we create an n-dimensional hedonic space based on the qualitative information available to consumers. Next, we allocate the differentiated products into this space and estimate the quantitative demand elasticities for these products using qualitative factor distances. What distinguishes our model from traditional demand estimation models is the way we link elasticities with products’ qualitative attributes. Moreover, in traditional demand systems, the number of estimated parameters increases exponentially with the number of variables included in the model. Our model significantly reduces the number of parameters to be estimated, thereby making it possible to estimate large number of differentiated products in a single demand system. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

A quality-based approach to estimating quantitative elasticities for differentiated products: an application to retail milk demand

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Publisher
Springer Netherlands
Copyright
Copyright © 2014 by Springer Science+Business Media Dordrecht
Subject
Social Sciences, general; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-014-0094-8
Publisher site
See Article on Publisher Site

Abstract

This article introduces the Hedonic Metric (HM) approach as an original method to model the demand for differentiated products using qualitative attributes. Our approach is based on a two-stage estimation procedure that utilizes qualitative characteristics of products. First, we create an n-dimensional hedonic space based on the qualitative information available to consumers. Next, we allocate the differentiated products into this space and estimate the quantitative demand elasticities for these products using qualitative factor distances. What distinguishes our model from traditional demand estimation models is the way we link elasticities with products’ qualitative attributes. Moreover, in traditional demand systems, the number of estimated parameters increases exponentially with the number of variables included in the model. Our model significantly reduces the number of parameters to be estimated, thereby making it possible to estimate large number of differentiated products in a single demand system.

Journal

Quality & QuantitySpringer Journals

Published: Sep 13, 2014

References

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