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Information Theory Estimators for the First-Order Spatial Autoregressive Model


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1

The Institute for Innovation, Tomsk State University of Control Systems and Radioelectronics, Tomsk 634050, Russia

2

School of Economic Sciences, Washington State University, PO Box 646210, Pullman, WA 99164, USA





*

Author to whom correspondence should be addressed.



Abstract Information theoretic estimators for the first-order spatial autoregressive model are introduced, small sample properties are investigated, and the estimator is applied empirically. Monte Carlo experiments are used to compare finite sample performance of more traditional spatial estimators to three different information theoretic estimators, including maximum empirical likelihood, maximum empirical exponential likelihood, and maximum log Euclidean likelihood. Information theoretic estimators are found to be robust to selected specifications of spatial autocorrelation and may dominate traditional estimators in the finite sample situations analyzed, except for the quasi-maximum likelihood estimator which competes reasonably well. The information theoretic estimators are illustrated via an application to hedonic housing pricing. View Full-Text

Keywords: information theoretic estimators; first order spatial autocorrelation; maximum empirical likelihood; maximum empirical exponential likelihood; maximum log-Euclidean likelihood; finite sample properties information theoretic estimators; first order spatial autocorrelation; maximum empirical likelihood; maximum empirical exponential likelihood; maximum log-Euclidean likelihood; finite sample properties





Autor: Evgeniy V. Perevodchikov 1, Thomas L. Marsh 2,* and Ron C. Mittelhammer 2

Fuente: http://mdpi.com/



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