Simultaneous equation models with spatially autocorrelated error components
Marius Amba () and
Taoufiki Mbratana
MPRA Paper from University Library of Munich, Germany
Abstract:
This paper develops estimators for simultaneous equations with spatial autoregressive or spatial moving average error components. We derive a limited information estimator and a full information estimator. We give the generalized method of moments to get each coefficient of the spatial dependence of each equation in spatial autoregressive case as well as spatial moving average case. The results of our Monte Carlo suggest that our estimators are consistent. When we estimate the coefficient of spatial dependence it seems better to use instrumental variables estimator that takes into account simultaneity. We also apply these set of estimators on real data.
Keywords: Panel data; SAR process; SMA process; Simultaneous equations; Spatial error components (search for similar items in EconPapers)
JEL-codes: C13 C33 (search for similar items in EconPapers)
Date: 2017-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-geo
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://mpra.ub.uni-muenchen.de/82395/1/MPRA_paper_82395.pdf original version (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:82395
Access Statistics for this paper
More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().