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Journal Articles Austrian Journal of Statistics Year : 2018

Interpretation of explanatory variables impacts in compositional regression models

Abstract

We are interested in modeling the impact of media investments on automobile manufacturer’s market shares. Regression models have been developed for the case where the dependent variable is a vector of shares. Some of them, from the marketing literature, are easy to interpret but quite simple (Model A). Other models, from the compositional data analysis literature, allow a large complexity but their interpretation is not straightforward (ModelB).This paper combines both literatures in order to obtain a performing market share model and develop relevant interpretations for practical use. We prove that Model A is a particular case of Model B, and that an intermediate specification is possible (Model AB). A model selection procedure is proposed. Several impact measures are presented and we show that elasticities are particularly useful: they can be computed from the transformed or from the original model, and they are linked to the simplicial derivatives.
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Dates and versions

hal-02617783 , version 1 (25-05-2020)

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Joanna Morais, Christine Thomas-Agnan, Michel Simioni. Interpretation of explanatory variables impacts in compositional regression models. Austrian Journal of Statistics, 2018, 47 (5), pp.1-25. ⟨10.17713/ajs.v47i5.718⟩. ⟨hal-02617783⟩
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