Innovation is one of the main leverages of regional economic development. It has been previously studied through classical methods (e.g., OLS) without considering the potential spatial heterogeneity influence. Local regression methods, such as geographically weighted regression (GWR), might describe the phenomenon more appropriately. The geographically weighted panel regression (GWPR) combines GWR with panel estimation controlling for spatial and individual heterogeneity as a methodological enhancement. This paper compares the estimates of GWPR, GWR and global models using data on 287 NUTS-2 European regions in 2014-2021. The results confirm that GWPR estimations significantly differ from GWR and global models, potentially producing new patterns and findings.

Evaluating the determinants of innovation from a spatio-temporal perspective. The GWPR approach

Musella G.;Rivieccio G.;Bruno E.
2022

Abstract

Innovation is one of the main leverages of regional economic development. It has been previously studied through classical methods (e.g., OLS) without considering the potential spatial heterogeneity influence. Local regression methods, such as geographically weighted regression (GWR), might describe the phenomenon more appropriately. The geographically weighted panel regression (GWPR) combines GWR with panel estimation controlling for spatial and individual heterogeneity as a methodological enhancement. This paper compares the estimates of GWPR, GWR and global models using data on 287 NUTS-2 European regions in 2014-2021. The results confirm that GWPR estimations significantly differ from GWR and global models, potentially producing new patterns and findings.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/109276
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