Computation of the Fisher information matrix for SISO models

Author

Klein, André ; Melard, Guy

Author_Institution

Dept. of Econ. Stat., Amsterdam Univ., Netherlands

Volume

42

Issue

3

fYear

1994

fDate

3/1/1994 12:00:00 AM

Firstpage

684

Lastpage

688

Abstract

Closed form expressions and an algorithm for obtaining the Fisher information matrix of Gaussian single input single output (SISO) time series models are presented. It enables the computation of the asymptotic covariance matrix of maximum likelihood estimators of the parameters. The procedure makes use of the autocovariance function of one or more autoregressive processes. Under certain conditions, the SISO model can be a special case of a vector autoregressive moving average (ARMA) model, for which there is a method to evaluate the Fisher information matrix. That method is compared with the procedure described in the paper

Keywords

information theory; matrix algebra; maximum likelihood estimation; parameter estimation; stochastic processes; time series; ARMA; Fisher information matrix; Gaussian time series; SISO models; asymptotic covariance matrix; autocovariance function; autoregressive processes; closed form expressions; maximum likelihood estimators; single input single output; vector autoregressive moving average; Autoregressive processes; Covariance matrix; Data analysis; Econometrics; Maximum likelihood estimation; Measurement errors; Parameter estimation; Signal processing; Time series analysis; Yield estimation;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.277866

Filename

277866