Allal, Jelloul and Kaaouachi, Abdelali and Paindaveine, Davy (2001): Restimation for ARMA models. Published in: Journal of Nonparametric Statistics No. 13 (2001): pp. 815831.

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Abstract
This paper is devoted to the Restimation problem for the parameter of a stationary ARMA model. The asymptotic uniform linearity of a suitable vector of rank statistics leads to the asymptotic normality of √nconsistent Restimates resulting from the minimization of the norm of this vector. By using a discretized √nconsistent preliminary estimate, we construct a new class of onestep Restimators. We compute the asymptotic relative efficiency of the proposed estimators with respect to the LS estimator. Efficiency properties are investigated via a MonteCarlo study in the particular case of an AR(1) model.
Item Type:  MPRA Paper 

Original Title:  Restimation for ARMA models 
Language:  English 
Keywords:  Restimation, ARMA models, local asymptotic normality, asymptotic linearity 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General 
Item ID:  21167 
Depositing User:  Davy Paindaveine 
Date Deposited:  07 Mar 2010 04:36 
Last Modified:  26 Mar 2017 23:48 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/21167 