Results 201 to 210 of about 51,335 (252)

A NOTE ON ARMA ESTIMATION

Journal of Time Series Analysis, 1983
Abstract. The ranks of certain matrices composed of autocovariances of an ARMA process are considered. These matrices arise in connection with initial estimates of the parameters of such a system. Conditions for such matrices to be of full rank are expressed in terms of conditions on a dual time reversed system.
An, Hong-Zhi, Chen, Zhao-Guo, Hannan, E. J.   +2 more
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The Identification of ARMA Models

Biometrika, 1990
SUMMARY We present a new powerful method for determining the order of ARMA (p, q) models having small sets of observations. The procedure is based on an autoregressive order determination criterion and on linear estimation methods. Simulated data are used to demonstrate the capabilities of the approach. for a survey.
TARMO PUKKILA, SERGIO KOREISHA, ARTO KALLINEN   +2 more
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Multichannel ARMA processes

IEEE Transactions on Signal Processing, 1994
Parametric modeling of multichannel time series is accomplished by using higher (than second) order statistics (HOS) of the observed nonGaussian data. Cumulants of vector processes are defined using a Kronecker product formulation, and consistency of their sample estimators is addressed.
Ananthram Swami, Georgios B. Giannakis, Sanyogita Shamsunder   +2 more
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A NOTE ON ARMA PARAMETERISATION*

Australian Journal of Statistics, 1995
SummaryThis paper considers the relationship between ARMA parameterisations of models fory(t)andAy(t), whereAis invertible andy(t)is a vector time series (t = 0,±1,…). An ARMA model for the transformed seriesAy(t)may have fewer parameters than a model fory(t).This paper shows that such a saving is illusory because the apparently saved parameters are ...
Hannan, E. J., Kavalieris, L.
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Spectral estimation of ARMA processes using ARMA-cepstrum recursion

IEEE Signal Processing Letters, 2000
In this letter, the spectral estimation problem of a stationary autoregressive moving average (ARMA) process is considered, and a new method for the estimation of the MA part is proposed. A simple recursion relating the ARMA parameters and the cepstral coefficients of an ARMA process is derived and utilized for the estimation of the MA parameters.
Kayhan, AS, Kaderli, A
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Forecasting Short Term Interest Rates Using ARMA, ARMA-GARCH and ARMA-EGARCH Models

SSRN Electronic Journal, 2006
Forecasting interest rates is of great concern for financial researchers, economists and players in the fixed income markets. The purpose of this study is to develop an appropriate model for forecasting the short-term interest rates i.e., commercial paper rate, implicit yield on 91 day treasury bill, overnight MIBOR rate and call money rate.
S. Radha, Thenmozhi M.
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Deterministic ARMA Models

1986
This paper deals with the ubiquitous class of linear time-invariant finite dimensional systems. In [1] it has been shown that this is precisely the class of AR systems, that is, the dynamical systems whose behavior is governed by a finite set of autoregressive relations.
NIEUWENHUIS, JW, WILLEMS, JC
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A metric for ARMA processes

IEEE Transactions on Signal Processing, 2000
Summary: Autoregressive-moving-average (ARMA) models seek to express a system function of a discretely sampled process as a rational function in the \(z\)-domain. Treating an ARMA model as a complex rational function, we discuss a metric defined on the set of complex rational functions.
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