Covariates impacts in spatial autoregressive models for compositional data

Thibault Laurent, Christine Thomas-Agnan and Anne Ruiz-Gazen
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Thibault Laurent: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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Abstract: Spatial autoregressive models have been adapted to model data with both a geographic and a compositional nature. Interpretation of parameters in such a model is intricate. Indeed, when the model involves a spatial lag of the dependent variable, this interpretation must focus on the so-called impacts rather than on parameters and when moreover the dependent variable of this model is of a compositional nature, this interpretation should be based on elasticities or semi-elasticities. Combining the two difficulties, we provide exact formulas for the evaluation of these elasticity-based impact measures which have been only approximated so far in some applications. We also discuss their decomposition into direct and indirect impacts taking into account the compositional nature of the dependent variable. Finally, we also propose more local summary measures as exploratory tools that we illustrate on a toy data set and on real data.

Keywords: Elasticities; Direct impact; Local impact; Indirect impact; Semi-elasticities; Simplicial regression (search for similar items in EconPapers)
Date: 2023-07
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Published in Journal of Spatial Econometrics, 2023, 4., ⟨10.1007/s43071-023-00035-0⟩

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Journal Article: Covariates impacts in spatial autoregressive models for compositional data (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04215280

DOI: 10.1007/s43071-023-00035-0

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