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Pré-Publication, Document De Travail (Working Paper) Année : 2017

Interpretation of explanatory variables impacts in compositional regression models

Joanna Morais
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  • PersonId : 1012843
Christine Thomas-Agnan
  • Fonction : Auteur
  • PersonId : 946985

Résumé

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 (Model B). 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 et versions

hal-01563362 , version 1 (17-07-2017)

Identifiants

  • HAL Id : hal-01563362 , version 1
  • PRODINRA : 407644

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Joanna Morais, Christine Thomas-Agnan, Michel Simioni. Interpretation of explanatory variables impacts in compositional regression models. 2017. ⟨hal-01563362⟩
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