RT Monograph
SR 00
A1 Nguyen, T.H.A
A1 Thomas-Agnan, Christine
A1 Laurent, Thibault
A1 Ruiz-Gazen, Anne
T1 A simultaneous spatial autoregressive model for compositional data
YR 2019
FD 2019-07
VO 19-1028
SP 13
K1 multivariate spatial autocorrelation
K1 spatial weight matrix
K1 three-stage least squares
K1 two-stage least squares
K1 simplex
K1 electoral data
K1 CoDa.
AB In an election, the vote shares by party on a given subdivision of a territory form a vector with positive components adding up to 1 called a composition. Using a conventional multiple linear regression model to explain this vector by some factors is not adapted for at least two reasons. The first one is the existence of the constraint on the sum of the components and the second one is the assumption of statistical independence across territorial units which may be questionable due to potential spatial autocorrelation. We develop a simultaneous spatial autoregressive model for compositional data which  allows for both spatial correlation and correlations across equations. We propose an estimation method based on two-stage and three-stage least squares. We illustrate the method with simulations and with a data set from the 2015 French departmental election.
T2 TSE Working Paper
PB TSE Working Paper
PP Toulouse
AV Published
LK https://publications.ut-capitole.fr/id/eprint/32640/
UL https://www.tse-fr.eu/sites/default/files/TSE/documents/doc/wp/2019/wp_tse_1028.pdf