on the publication serverTitle 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
(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 > Chair Economics VI: Empirical Economic Research Faculties > Faculty of Law, Business and Economics > Department of Economics > Chair Economics VI: Empirical Economic Research > Chair Economics VI: Empirical Economic Research - 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: | 20 Jan 2022 13:02 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/16666 |
