Recursive estimation of possibly misspecified MA(1) models: Convergence of a general algorithm
Abstract
We introduce a recursive algorithm of conveniently general form for estimating the coefficient of a moving average model of order one and obtain convergence results for both correct and misspecified MA(1) models. The algorithm encompasses Pseudolinear Regression (PLR--also referred to as AML and ) and Recursive Maximum Likelihood () without monitoring. Stimulated by the approach of Hannan (1980), our convergence results are obtained indirectly by showing that the recursive sequence can be approximated by a sequence satisfying a recursion of simpler (Robbins-Monro) form for which convergence results applicable to our situation have recently been obtained.
Cite
@article{arxiv.math/0702764,
title = {Recursive estimation of possibly misspecified MA(1) models: Convergence of a general algorithm},
author = {James L. Cantor and David F. Findley},
journal= {arXiv preprint arXiv:math/0702764},
year = {2007}
}
Comments
Published at http://dx.doi.org/10.1214/074921706000000932 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)