A Bayesian spatial autoregressive logit model with an empirical application to European regional FDI flows

In this paper we propose a Bayesian estimation approach for a spatial autoregressive logit specification. Our approach relieson recent advances in Bayesian computing, making use of Pólya-Gamma sampling for Bayesian Markov-chain Monte Carlo algorithms.The proposed specification assumes that the involved log-odds of the model follow a spatial autoregressive process. Pólya-Gammasampling involves a computationally efficient treatment of the spatial autoregressive logit model, allowing for extensionsto the existing baseline specification in an elegant and straightforward way. In a Monte Carlo study we demonstrate that ourproposed approach significantly outperforms existing spatial autoregressive probit specifications both in terms of parameterprecision and computational time. The paper moreover illustrates the performance of the proposed spatial autoregressive logitspecification using pan-European regional data on foreign direct investments. Our empirical results highlight the importanceof accounting for spatial dependence when modelling European regional FDI flows.