This article is cited in 1 paper

Estimation and Filtering

Estimating the generalized autoregression model parameters for unknown noise distribution

A. A. Malyarenko

Tomsk State University

Abstract: We solve the problem of estimating the autoregressive parameters of a nonlinear stable stochastic process with discrete time of the AR($p$)/ARCH($p$) type with unknown ARCH($p$) process parameters. For the AR(1)/ARCH(1) model, we solve the estimation problem for all unknown process parameters, i.e., the autoregression parameter and two parameters of the noise process ARCH(1). We assume that the noise distributions are unknown. We show that the least square estimates are strongly consistent.

PACS: 02.50.Fz

Presented by the member of Editorial Board: A. I. Kibzun

Received: 02.03.2009

 English version: Automation and Remote Control, 2010, 71:2, 291–302

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