Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/207159 
Year of Publication: 
2019
Series/Report no.: 
WIFO Working Papers No. 586
Publisher: 
Austrian Institute of Economic Research (WIFO), Vienna
Abstract: 
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.
Subjects: 
Spatial autoregressive logit
Bayesian MCMC estimation
FDI flows
European regions
JEL: 
C11
C21
C25
F23
R11
R30
Document Type: 
Working Paper

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