MULTIPARAMETRIC AND HIERARCHICAL SPATIAL AUTOREGRESSIVE MODELS: THE EVALUATION OF THE MISSPECIFICATION OF SPATIAL EFFECTS USING A MONTE CARLO SIMULATION
Keywords:
Spatial model, hierarchical model, Monte Carlo, Bayesian estimation.Abstract
The aim of this paper is to evaluate the spatial and hierarchical models for data generating processes with spatial heterogeneity and spatial dependence at the higher level. The simulation for the m-SAR and HSAR models was used to discuss the consequences of spatial misspecification. We noticed that the misspecification of spatial homogeneity or heterogeneity in both models affects i.a. the estimated parameter for spatial interactions at the individual level. Applying a m-SAR model for spatially heterogeneous processes causes the overestimation of the spatial interaction parameter.
Downloads
References
Anselin L. (1988), Spatial Econometrics: Methods and Models, Vol. 4. Springer.
Google Scholar
Baltagi B. H., Fingleton B., Pirotte A. (2014), Spatial lag models with nested random effects: An instrumental variable procedure with an application to English house prices, “Journal of Urban Economics”, 80, pp. 76-86.
Google Scholar
Chasco C., Le Gallo J. (2012), Hierarchy and spatial autocorrelation effects in hedonic models, “Economics Bulletin”, 32 (2), pp. 1474-1480.
Google Scholar
Corrado L., Fingleton B. (2012), Where is the economics in spatial econometrics?, “Journal of Regional Science”, 52(2), pp. 210-239.
Google Scholar
Dong G., Harris R. J. (2014), Spatial Autoregressive Models for Geographically Hierarchical Data Structures, “Geographical Analysis”.
Google Scholar
Elhorst J. P., Lacombe D. J., Piras G. (2012), On model specification and parameter space definitions in higher order spatial econometric models, “Regional Science and Urban Economics”, 42 (1), pp. 211-220.
Google Scholar
Getis A., Fischer M. M. (2010), Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications, Springer.
Google Scholar
Goldstein H. (2011), Multilevel statistical models, vol. 922, John Wiley & Sons.
Google Scholar
Hays J. C., Kachi A., Franzese Jr. R. J. (2010), A spatial model incorporating dynamic, endogenous network interdependence: A political science application, “Statistical Methodology”, 7 (3), pp. 406-428.
Google Scholar
Hepple L. W. (1995), Bayesian techniques in spatial and network econometrics: 2. Computational methods and algorithms, “Environment and Planning A”, 27(4), pp. 615-644.
Google Scholar
Hoogland J., Boomsma A. (1998), Robustness studies in covariance structure modeling: An 14 overview and a meta-analysis, “Sociological Methods and Research”, 26(3).
Google Scholar
Łaszkiewicz E. (2013), Sample size and structure for multilevel modelling: Monte Carlo investigation for the balanced design, “Metody Ilościowe w Badaniach Ekonomicznych”, XIV-2”, pp. 19-28.
Google Scholar
López-Hernández F. A. (2013), Second-order polynomial spatial error model. Global and local spatial dependence in unemployment in Andalusia, “Economic Modelling”, 33, pp. 270-279.
Google Scholar
Lottmann F. (2013), Spatial dependence in German labor markets, Doctoral dissertation, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät.
Google Scholar
Olejnik A. (2009), Metodologia i zastosowania modeli przestrzenno-autoregresyjych w badaniach rozwoju regionalnego, Doctoral dissertation, University of Lodz.
Google Scholar