Robustness of Spatial Autocorrelation Specifications: Some Monte Carlo Evidence
AbstractThis paper examines the robustness of various models of spatial autocorrelation through a series of Monte Carlo experiments in which each model takes a turn at the data generator. The generated data are then used to estimate all of the models. The estimated models are evaluated primarily on their predictive power. Copyright Blackwell Publishing, Inc 2003
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Bibliographic InfoArticle provided by Wiley Blackwell in its journal Journal of Regional Science.
Volume (Year): 43 (2003)
Issue (Month): 2 ()
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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0022-4146
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- Kelejian, Harry H. & Prucha, Ingmar R., 2007. "The relative efficiencies of various predictors in spatial econometric models containing spatial lags," Regional Science and Urban Economics, Elsevier, vol. 37(3), pages 363-374, May.
- Jesus Mur & Ana Angulo, 2005. "A closer look at the Spatial Durbin Model," ERSA conference papers ersa05p392, European Regional Science Association.
- Takafumi Kato, 2013. "Usefulness of the Information Contained in the Prediction Sample for the Spatial Error Model," The Journal of Real Estate Finance and Economics, Springer, vol. 47(1), pages 169-195, July.
- Yong Tu & Seow Ong & Ying Han, 2009. "Turnovers and Housing Price Dynamics: Evidence from Singapore Condominium Market," The Journal of Real Estate Finance and Economics, Springer, vol. 38(3), pages 254-274, April.
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