Explicit expressions are derived for the gradient vector and (approximate) Hessian matrix of
the log likelihood function for the multivariate autoregressive moving‐average (ARMA) …

In this paper we present a generalized least‐squares approach for estimating
autoregressive moving‐average (ARMA) models. Simulation results based on different …

Some simple preliminary estimators for the coefficients of mixed autoregressive moving
average time series models are considered. As the first step the estimators require the fitting …

Procedures to estimate parameters in multivariate autoregressive moving average (ARMA)
models are developed. Gaussian errors are assumed. Exact maximum likelihood estimation …

In time series analysis, the basic univariate model is the autoregressive moving average
(ARMA) one. The estimation of ARMA models has been the subject of a vast literature over …

We present a method for estimating regression models with autoregressive integrated
moving average (ARIMA) time series errors. The method maximizes the likelihood for …

This paper investigates theoretical aspects of the relationship between the generalized least
squares and Gaussian estimation schemes for vector autoregressive moving‐average …

The paper provides a method for the computation of the derivatives of the exact log
likelihood function of a Gaussian time series. Based on this result and using Fisher's scoring …

University of Missouri 1. Introduction. The methods employed in least squares parameter
estimation for moving average (MA) processes differ from those appropriate for …

In this paper, we call attention of two observed features in practical applications of the
Generalized Autoregressive Moving Average (GARMA) model due to the structure of its …