Skip to main content

A recursive three-stage least squares method for large-scale systems of simultaneous equations

Hadjiantoni, Stella, Kontoghiorghes, Erricos John (2017) A recursive three-stage least squares method for large-scale systems of simultaneous equations. Linear Algebra and its Applications, 536 . pp. 210-227. ISSN 0024-3795. (doi:10.1016/j.laa.2017.08.019) (KAR id:64226)

PDF Author's Accepted Manuscript
Language: English


Download (284kB) Preview
[thumbnail of Manuscript_SHadjiantoni.pdf]
Preview
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL
https://doi.org/10.1016/j.laa.2017.08.019

Abstract

A new numerical method is proposed that uses the QR decomposition (and its variants) to derive recursively the three-stage least squares (3SLS) estimator of large-scale simultaneous equations models (SEM). The 3SLS estimator is obtained sequentially, once the underlying model is modified, by adding or deleting rows of data. A new theoretical pseudo SEM is developed which has a non positive definite dispersion matrix and is proved to yield the 3SLS estimator that would be derived if the modified SEM was estimated afresh. In addition, the computation of the iterative 3SLS estimator of the updated observations SEM is considered. The new recursive method utilizes efficiently previous computations, exploits sparsity in the pseudo SEM and uses as main computational tool orthogonal and hyperbolic matrix factorizations. This allows the estimation of large-scale SEMs which previously could have been considered computationally infeasible to tackle. Numerical trials have confirmed the effectiveness of the new estimation procedures. The new method is illustrated through a macroeconomic application.

Item Type: Article
DOI/Identification number: 10.1016/j.laa.2017.08.019
Uncontrolled keywords: UpdatingQR decompositionHigh dimensional dataMatrix algebra
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Stella Hadjiantoni
Date Deposited: 03 Nov 2017 10:22 UTC
Last Modified: 16 Feb 2021 13:49 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/64226 (The current URI for this page, for reference purposes)
  • Depositors only (login required):

Downloads

Downloads per month over past year