Parameter spaces for stationary DGPs in spatial econometric modelling
Unlike the time series literature the spatial econometric literature has not really dealt with the issue of the parameter space. This paper shows that current parameter space concepts for spatial econometric DGPs are inadequate. It proves that the parameter space proposed by Kelejian and Prucha 2008 can result in nonstationary DGPs, while the parameter space proposed by Lee and Liu 2010 can be too restrictive in applied cases. Furthermore it is discussed that the practice of row standardizing lacks a mathematical foundation. Due to these problems concerning the current parameter space consepts, this paper provides a new deâ€¦nition for the spatial econometric parameter space. It is able to show which assumptions are necessary to give row standardizing the needed mathematical foundation. Finally two additional applications for the new parameter space deâ€¦nition concerning models with group interaction and panels with fixed cross section sample size are provided. Both applications result in parameter spaces that are substantially larger than the ones the literature would so far considered to be stationary.
References listed on IDEAS
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- Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
- Kelejian, Harry H & Prucha, Ingmar R, 1998.
"A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances,"
The Journal of Real Estate Finance and Economics,
Springer, vol. 17(1), pages 99-121, July.
- Harry H. Kelejian & Ingmar R. Prucha, 1997. "A Generalized Spatial Two Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," Electronic Working Papers 97-002, University of Maryland, Department of Economics, revised Aug 1997.
- J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
- Lee, Lung-fei & Liu, Xiaodong, 2010. "Efficient Gmm Estimation Of High Order Spatial Autoregressive Models With Autoregressive Disturbances," Econometric Theory, Cambridge University Press, vol. 26(01), pages 187-230, February. Full references (including those not matched with items on IDEAS)
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