Bayesian Estimation of the Functional Spatial Lag Model

Aw Alassane () and Cabral Emmanuel Nicolas
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Cabral Emmanuel Nicolas: Department of Mathematics, Assane Seck University of Ziguinchor, Ziguinchor, Senegal

Journal of Time Series Econometrics, 2020, vol. 12, issue 2, 22

Abstract: The spatial lag model (SLM) has been widely studied in the literature for spatialised data modeling in various disciplines such as geography, economics, demography, regional sciences, etc. This is an extension of the classical linear model that takes into account the proximity of spatial units in modeling. In this paper, we propose a Bayesian estimation of the functional spatial lag (FSLM) model. The Bayesian MCMC technique is used as a method of estimation for the parameters of the model. A simulation study is conducted in order to compare the results of the Bayesian functional spatial lag model with the functional spatial lag model and the functional linear model. As an illustration, the proposed Bayesian functional spatial lag model is used to establish a relationship between the unemployment rate and the curves of illiteracy rate observed in the 45 departments of Senegal.

Keywords: spatial lag model; functional data analysis; Bayesian estimation; MCMC algorithms; 62R10; 62H11; 62F15; 62P20; 60J22 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1515/jtse-2019-0047

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