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A TWO-STEP MAXIMUM - LIKELIHOOD IDENTIFICATION OF NON-GAUSSIAN SYSTEMS
Gerencsér, László* Michaletzky, György** Reppa, Zoltán***
* Computer and Automation Institute of the Hungarian Academy of
Sciences.
** Eötvös Loránd University, Dept. of Probability Theory and
Statistics.
*** Eötvös Loránd University, Dept. of Probability Theory and
Statistics, Computer and Automation Institute of the Hungarian
Academy of Sciences, and National Bank of Hungary.
ARMA modelling of many economic time series leads to processes with heavy-tailed marginal distribution. We present methods of estimating the parameters of such processes. Asymptotic properties of the full information maximum likelihood and partially adaptive estimates are discussed. We give an asymptotic description of the estimation error process in both cases.
Keywords: Linear systems, non - Gaussian processes, identification, ARMA parameter estimation
Session slot T-Th-M21: Posters of Design Methods and Optimal Control/Area code 2b : Linear Systems
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