In this paper, we propose a recursive approach to estimate the spatial error model. We compare the suggested methodology with standard estimation procedures and we report a set of Monte Carlo experiments which show that the recursive approach substantially reduces the computational effort affecting the precision of the estimators within reasonable limits. The proposed technique can prove helpful when applied to real-time streams of geographical data that are becoming increasingly available in the big data era. Finally, we illustrate this methodology using a set of earthquake data.

Ghiringhelli, C., Piras, G., Arbia, G., Mira, A., (Abstract) Recursive Estimation of the Spatial Error Model, <<GEOGRAPHICAL ANALYSIS>>, 2022; 2022 (N/A): N/A-N/A. [doi:10.1111/gean.12317] [https://hdl.handle.net/10807/205022]

Recursive Estimation of the Spatial Error Model

Ghiringhelli, Chiara
Secondo
;
Arbia, Giuseppe
Primo
;
2022

Abstract

In this paper, we propose a recursive approach to estimate the spatial error model. We compare the suggested methodology with standard estimation procedures and we report a set of Monte Carlo experiments which show that the recursive approach substantially reduces the computational effort affecting the precision of the estimators within reasonable limits. The proposed technique can prove helpful when applied to real-time streams of geographical data that are becoming increasingly available in the big data era. Finally, we illustrate this methodology using a set of earthquake data.
2022
Inglese
Ghiringhelli, C., Piras, G., Arbia, G., Mira, A., (Abstract) Recursive Estimation of the Spatial Error Model, <<GEOGRAPHICAL ANALYSIS>>, 2022; 2022 (N/A): N/A-N/A. [doi:10.1111/gean.12317] [https://hdl.handle.net/10807/205022]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/205022
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