6533b834fe1ef96bd129dfe5
RESEARCH PRODUCT
Nonlinear impact estimation in spatial autoregressive models
Kassoum AyoubaJulie Le GalloJean-sauveur Aysubject
Economics and Econometrics[SDV]Life Sciences [q-bio]Lag0507 social and economic geographysymbols.namesake0502 economics and businessEconometricsMarginal impacts050207 economicsSpatial econometricsMathematics05 social sciencesMarkov chain Monte Carlo[SHS.ECO]Humanities and Social Sciences/Economics and FinanceSplineConfidence intervalMarkov chain Monte CarloSpline (mathematics)Nonlinear systemAutoregressive model13. Climate actionsymbolsBayesian frameworkSpatial econometrics050703 geographyFinancedescription
International audience; This paper extends the literature on the calculation and interpretation of impacts for spatial autoregressive models. Using a Bayesian framework, we show how the individual direct and indirect impacts associated with an exogenous variable introduced in a nonlinear way in such models can be computed, theoretically and empirically. Rather than averaging the individual impacts, we suggest to graphically analyze them along with their confidence intervals calculated from Markov chain Monte Carlo (MCMC). We also explicitly derive the form of the gap between individual impacts in the spatial autoregressive model and the corresponding model without a spatial lag and show, in our application on the Boston dataset, that it is higher for spatially highly connected observations.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-02-01 | Economics Letters |