Spatial System Estimators for Panel Models: A Sensitivity and Simulation Study
Shuangzhe Liu,
Tiefeng Ma and
Wolfgang Polasek
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Shuangzhe Liu: University of Canberra, Canberra, Australia
Tiefeng Ma: Statistics College, Southwestern University of Finance and Economics, Chengdu, China
No 294, Economics Series from Institute for Advanced Studies
Abstract:
System of panel models are popular models in applied sciences and the question of spatial errors has created the recent demand for spatial system estimation of panel models. Therefore we propose new diagnostic methods to explore if the spatial component will change significantly the outcome of non-spatial estimates of seemingly unrelated regression (SUR) systems. We apply a local sensitivity approach to study the behavior of generalized least squares (GLS) estimators in two spatial autoregression SUR system models: a SAR model with SUR errors (SAR-SUR) and a SUR model with spatial errors (SUR-SEM). Using matrix derivative calculus we establish a sensitivity matrix for spatial panel models and we show how a first order Taylor approximation of the GLS estimators can be used to approximate the GLS estimators in spatial SUR models. In a simulation study we demonstrate the good quality of our approximation results.
Keywords: Seemingly unrelated regression models; panel systems with spatial errors; SAR and SEM models; generalized least-squares estimators; Taylor approximations (search for similar items in EconPapers)
JEL-codes: C22 G14 G15 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2012-12
New Economics Papers: this item is included in nep-ecm and nep-ure
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https://irihs.ihs.ac.at/id/eprint/2177 First version, 2012 (application/pdf)
Related works:
Journal Article: Spatial system estimators for panel models: A sensitivity and simulation study (2014) 
Working Paper: Spatial System Estimators for Panel Models: A Sensitivity and Simulation Study (2013) 
Working Paper: Spatial System Estimators for Panel Models: A Sensitivity and Simulation Study (2012) 
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