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1.	Andersson, Martin, et al. (author) Spatial dependence and the representation of space in empirical models 2009 In: The annals of regional science. - : Springer Science and Business Media LLC. - 0570-1864 .- 1432-0592. ; 43:1, s. 159-180 Journal article (peer-reviewed)abstract A well-formed spatial model should most likely not produce spatial autocorrelation at all. From this perspective spatial autocorrelation is not (pure) statistical nuisance but a sign of that a model lacks a representation of an important economic phenomenon. In a Knowledge Production Function (KPF) context, this paper shows that a representation of space reflecting the potential of physical interaction between localities by means of accessibility variables on the "right-hand-side"aEuro"a simple alternative to spatial lag and spatial error which can be estimated by OLS-captures substantive spatial dependence. Results are verified with Monte Carlo simulations based on Anselin's (Int Reg Sci Rev 26(2):153-166, 2003) taxonomy of modelled and unmodelled effects. The analysis demonstrates that an accessibility representation of explanatory variables depict the network nature of spatial interaction, such that spatial dependence is actually modelled.

Andersson, Martin, et al. (author)
Spatial dependence and the representation of space in empirical models
2009
In: The annals of regional science. - : Springer Science and Business Media LLC. - 0570-1864 .- 1432-0592. ; 43:1, s. 159-180
Journal article (peer-reviewed)abstract
- A well-formed spatial model should most likely not produce spatial autocorrelation at all. From this perspective spatial autocorrelation is not (pure) statistical nuisance but a sign of that a model lacks a representation of an important economic phenomenon. In a Knowledge Production Function (KPF) context, this paper shows that a representation of space reflecting the potential of physical interaction between localities by means of accessibility variables on the "right-hand-side"aEuro"a simple alternative to spatial lag and spatial error which can be estimated by OLS-captures substantive spatial dependence. Results are verified with Monte Carlo simulations based on Anselin's (Int Reg Sci Rev 26(2):153-166, 2003) taxonomy of modelled and unmodelled effects. The analysis demonstrates that an accessibility representation of explanatory variables depict the network nature of spatial interaction, such that spatial dependence is actually modelled.

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