Spatial Aspects in the Multilevel Models Construction

Edyta Łaszkiewicz

doi:10.18778/0208-6018.292.05

Authors

Edyta Łaszkiewicz University of Lodz

DOI:

https://doi.org/10.18778/0208-6018.292.05

Abstract

Multilevel (hierarchical) models are used for analysing data for which getting a few levels of the aggregation is possible. In the simplest case it is possible to present the way of organizing the levels in the form of the hierarchical structure or applying the cross-classification of data. The multilevel model construction might be used in the spatial analyses. The purpose of this article is to present the possibility of spatial processes analyses using multilevel models. The implementation techniques of the already existing multilevel models to the spatial structure were discussed. Additionally, the possibility of the traditional multilevel models rebuilding, towards taking into account spatial interactions, was present.

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References

Abreu M., De Groot H.L.F., Florax R.J.G.M., (2005), Space and growth: a survey of empirical evidence and methods, (in:) Region et Development, No. 21.
Google Scholar DOI: https://doi.org/10.2139/ssrn.631007

Anselin L. (1988), Spatial Econometrics: Methods and Models, Springer, Vol. 4.
Google Scholar DOI: https://doi.org/10.1007/978-94-015-7799-1

Anselin L., (2003), Spatial Externalities, Spatial Multipliers, And Spatial Econometrics (in:) International Regional Science Review, Vol. 26(2).
Google Scholar DOI: https://doi.org/10.1177/0160017602250972

Arbia G., Battisti M., Di Vaio G., (2010), Institutions and geography: Empirical test of spatial growth models for European regions, (in:) Economic Modelling, Vol. 27.
Google Scholar DOI: https://doi.org/10.1016/j.econmod.2009.07.004

Armstrong Ft., (1995), Convergence among regions of the European Union, 1950-1990, (in:) Papers in Regional Science, Vol. 74.
Google Scholar DOI: https://doi.org/10.1111/j.1435-5597.1995.tb00633.x

Blume L. E., Brock W. A., Durlauf, S. N., Ioannides, Y. M., (2011), Identification of social interactions, Handbook of Social Economics, North-Holland.
Google Scholar DOI: https://doi.org/10.2139/ssrn.1660002

Braumolle Y., Djebbari H., Fortin B., (2009), Identification of peer effects through social networks, (in:) Journal of Econometrics, No. 150.
Google Scholar DOI: https://doi.org/10.1016/j.jeconom.2008.12.021

Brock W.A., Durlauf S.N., (2001), Interaction-based Models, NBER Technical Working Papers 0258.
Google Scholar DOI: https://doi.org/10.3386/t0258

Capello R., Nijkamp P., (2009), Handbook of Regional Growth and Development Theories, Edward Elgar Publishing Limited.
Google Scholar DOI: https://doi.org/10.4337/9781848445987

Chasco C., Le Galio J., (2012), Hierarchy and spatial autocorrelation effects in hedonic models, (in:) Economics Bulletin, Vol. 32.2.
Google Scholar

Cohen-Cole E., (2006), Multiple groups identification in the linear-in-means model, (in:) Economics Letters, Vol. 92(2).
Google Scholar DOI: https://doi.org/10.1016/j.econlet.2006.01.035

Corrado L., Distante R., (2012), Eating Behavior and Social Interactions from Adolescence to Adulthood, Discussion Papers Department of Economics University of Copenhagen.
Google Scholar DOI: https://doi.org/10.2139/ssrn.2098920

Corrado L., Fingleton B., (2012), Where is the economics in spatial econometrics?, (in:) Journal of Regional Science, Vol. 52(2).
Google Scholar DOI: https://doi.org/10.1111/j.1467-9787.2011.00726.x

Ferron J., (1997), Moving Between Hierarchical Modeling Notations, (in:) Journal of educational and behavioral statistics, Vol. 22.
Google Scholar DOI: https://doi.org/10.2307/1165241

Goldstein H. (1999) Multilevel Statistical Models, Wiley.
Google Scholar

Grab J., (2009), Econometric analysis in poverty research: with case studies from developing countries, Peter Lang.
Google Scholar

Graham B., Hahn J., (2005), Identification and estimation of the linear-in-means model of social interactions, (in:) Economics Letters, Vol. 88.
Google Scholar DOI: https://doi.org/10.1016/j.econlet.2005.02.001

Griffith, D. A., (1992), A Spatially Adjusted N-Way ANOVA Model, (in:) Regional Science and Urban Economics, Vol. 22.
Google Scholar DOI: https://doi.org/10.1016/0166-0462(92)90034-X

Hays J.C., Kachi A., Franzese R.J., (2009), A spatial model incorporating dynamic, endogenous network interdependence: A political science application, (in:) Statistical Methodology, Vol. 7 (3).
Google Scholar DOI: https://doi.org/10.1016/j.stamet.2009.11.005

Ioannides Y.M., Topa G., (2010), Neighborhood effects: accomplishments and looking beyond them, (in:) Journal of Regional Science, Vol. 50, No. 1.
Google Scholar DOI: https://doi.org/10.1111/j.1467-9787.2009.00638.x

Lee L., (2007), Identification and Estimation of Spatial Econometric Models with Group Interactions, Contextual Factors and Fixed Effects, (in:) Journal of Econometrics, Vol. 140.2.
Google Scholar DOI: https://doi.org/10.1016/j.jeconom.2006.07.001

Manski Ch., (1993), Identification of Endogenous Social Effects: The Reflection Problem, (in:) The Review of Economic Studies, Vol. 60, No. 3.
Google Scholar DOI: https://doi.org/10.2307/2298123

Moffitt R., (2001), Policy Interventions, Low-Level Equilibra, and Social Interactions, (in:) Durlauf S., Young H. (Eds.), Social dynamics, MIT Press, Cambridge.
Google Scholar DOI: https://doi.org/10.7551/mitpress/6294.003.0005

Snijders T.A.B., Bosker R.J., (2011), Multilevel analysis: An introduction to basic and advanced multilevel modelling, Sage Publications Limited.
Google Scholar

Steenbergen M., Jones B., (2002), Modeling Multilevel Data Structures, (in:) American Journal of Political Science, Vol. 46, No. 1.
Google Scholar DOI: https://doi.org/10.2307/3088424

Zeilstra A.S., (2008), Regional labour markets in a cross-country perspective, PPI Publishers,available: http://irs.ub.rug.n1/ppn/314552685
Google Scholar