Nonlinear impact estimation in spatial autoregressive models
Jean-Sauveur Ay (),
Kassoum Ayouba and
Julie Le Gallo ()
Economics Letters, 2018, vol. 163, issue C, 59-64
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.
Keywords: Spatial econometrics; Marginal impacts; Spline; Markov chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 C21 (search for similar items in EconPapers)
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Working Paper: Nonlinear impact estimation in spatial autoregressive models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:163:y:2018:i:c:p:59-64
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