Asymptotic Properties of the LSE of a Spatial Regression in both Weakly and Strongly Dependent Stationary Random Fields

Yoshihiro Yajima and Yasumasa Matsuda
Additional contact information
Yoshihiro Yajima: Faculty of Economics, University of Tokyo
Yasumasa Matsuda: Faculty of Economics, Tohoku University

No CIRJE-F-587, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

Abstract: We consider asymptotic properties of the least squares estimator(LSE) in spatial regression with correlated errors. Firstly we derive sufficient conditions for the LSE to be strongly consistent and next necessary and/or sufficient conditions tor the LSE to be asymptotically efficient relative to the best liner unbiased estimator(BLUE). Finally we derive the asymptotic distribution of the LSE under conditions on the higher order cumulants of the error terms and the Fourier transforms of the regressors. The main feature is that we propose a unified way in which we can deal with both weakly dependent and strongly dependent error terms.

Date: Written 2008-09

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Access Statistics for this paper

More papers in CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo
Series data maintained by ().