Spatial System Estimators for Panel Models: A Sensitivity and Simulation Study
AbstractSystem 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.
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Bibliographic InfoPaper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 75_12.
Date of creation: Nov 2012
Date of revision:
Seemingly unrelated regression models; panel systems with spatial errors; SAR and SEM models; generalized least-squares estimators; Taylor approximations;
Other versions of this item:
- Shuangzhe Liu & Tiefeng Ma & Wolfgang Polasek, 2013. "Spatial System Estimators for Panel Models: A Sensitivity and Simulation Study," Working Paper Series 05_13, The Rimini Centre for Economic Analysis.
- Liu, Shuangzhe & Ma, Tiefeng & Polasek, Wolfgang, 2012. "Spatial System Estimators for Panel Models: A Sensitivity and Simulation Study," Economics Series 294, Institute for Advanced Studies.
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
- G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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