Recursive estimation of possibly misspecified MA(1) models: Convergence of a general algorithm

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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 $RML_1$) and Recursive Maximum Likelihood ($RML_2$) 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.
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)

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Statistics Theory, 62M10 (Primary) 62L20 (Secondary)

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http://salesiana.dossiersoluciones.com/handle/123456789/131377

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/math/0702764
 http://arxiv.org/abs/math/0702764
 IMS Lecture Notes Monograph Series 2006, Vol. 52, 20-47
 doi:10.1214/074921706000000932

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arXiv