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Performance Contest Between MLE and GMM for Huge Spatial Autoregressive Models

Title data

Larch, Mario ; Tappeiner, Gottfried ; Walde, Janette:
Performance Contest Between MLE and GMM for Huge Spatial Autoregressive Models.
In: Journal of Statistical Computation and Simulation. Vol. 78 (February 2008) Issue 2 . - pp. 151-166.
ISSN 1563-5163
DOI: https://doi.org/10.1080/10629360600954109

Abstract in another language

When using maximum likelihood estimation for spatial models, a well known problem is the computation of the logarithm of the determinant of the Jacobian, especially for problems with a huge number of observation units. In the recent literature there are various promising approaches to account for these numerical difficulties, relying on alternative decompositions or approximations. Recently, a general method of moments approach for estimating these models was developed. We compare all these different approaches with respect to their root mean-squared errors of the estimates and investigate the size and power of hypotheses tests with respect to the spatial correlation and the regression parameters.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Spatial statistics; Maximum likelihood; Generalized method of moments; Spatial auto-correlation; Jacobian
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Economics > Lehrstuhl Volkswirtschaftslehre VI: Empirische Wirtschaftsforschung
Faculties > Faculty of Law, Business and Economics > Department of Economics > Lehrstuhl Volkswirtschaftslehre VI: Empirische Wirtschaftsforschung > Lehrstuhl für Volkswirtschaftslehre VI: Empirische Wirtschaftsforschung - Univ.-Prof. Dr. Mario Larch
Faculties
Faculties > Faculty of Law, Business and Economics
Faculties > Faculty of Law, Business and Economics > Department of Economics
Result of work at the UBT: No
DDC Subjects: 300 Social sciences > 330 Economics
Date Deposited: 15 Oct 2015 10:59
Last Modified: 15 Oct 2015 10:59
URI: https://eref.uni-bayreuth.de/id/eprint/16666