Spatial models have been widely applied in the context of growth regressions with spatial spillovers usually modelled by simultaneous autoregressions (SAR). Although largely used, such a class of models present some logical difficulties connected with the error behaviour, the lack of identifiability of the model parameters and their substantive interpretation. To overcome these logical pitfalls, in this paper we introduce a new specification of regional growth regressions by applying multivariate Gaussian Markov random fields (GMRFs). We discuss the theoretical properties of the proposed model and show some empirical results on the economic growth pattern of 254 NUTS-2 European regions in the period 1992–2006. We show that the proposed GMRF model is able to capture the complexity of the phenomenon including the possibility of estimating site-specific convergence parameters which may highlight clustering of regions and spatial heterogeneities in the speed of convergence.

Romagnoli, L., Ippoliti, L., Arbia, G., a Gaussian Markov random field approach to convergence analysis, <<SPATIAL STATISTICS>>, 2013; 2013 (6): 78-90 [http://hdl.handle.net/10807/56654]

a Gaussian Markov random field approach to convergence analysis

Arbia, Giuseppe
2013

Abstract

Spatial models have been widely applied in the context of growth regressions with spatial spillovers usually modelled by simultaneous autoregressions (SAR). Although largely used, such a class of models present some logical difficulties connected with the error behaviour, the lack of identifiability of the model parameters and their substantive interpretation. To overcome these logical pitfalls, in this paper we introduce a new specification of regional growth regressions by applying multivariate Gaussian Markov random fields (GMRFs). We discuss the theoretical properties of the proposed model and show some empirical results on the economic growth pattern of 254 NUTS-2 European regions in the period 1992–2006. We show that the proposed GMRF model is able to capture the complexity of the phenomenon including the possibility of estimating site-specific convergence parameters which may highlight clustering of regions and spatial heterogeneities in the speed of convergence.
Inglese
Romagnoli, L., Ippoliti, L., Arbia, G., a Gaussian Markov random field approach to convergence analysis, <<SPATIAL STATISTICS>>, 2013; 2013 (6): 78-90 [http://hdl.handle.net/10807/56654]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/56654
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