Covariates impacts in spatial autoregressive models for compositional data

Christine Thomas-Agnan, Thibault Laurent and Anne Ruiz-Gazen

No 20-1162, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: Spatial simultaneous 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 a case study.

Keywords: Elasticities; direct impact; local impact; indirect impact; semi-elasticities; simplicial regression (search for similar items in EconPapers)
JEL-codes: C10 C39 C65 M31 Q15 (search for similar items in EconPapers)
Date: 2020-11-17, Revised 2021-10
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Covariates impacts in spatial autoregressive models for compositional data (2023)
Working Paper: Covariates impacts in spatial autoregressive models for compositional data (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:124927

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