Recently, a new estimator—Arfit—for multivariate (vector) autoregressive (MVAR)
parameters has been proposed. Several other MVAR estimators (eg Levinson recursion …

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

Durbin's methods (1959, 1960) for moving average (MA) and autoregressive-moving
average (ARMA) estimation use the parameters of a long AR model to compute the MA …

The correlation analysis based methods are not suitable for identifying parameters of
nonstationary autoregressive (AR), moving average (MA), and ARMA systems. By using …

The algorithm proposed here is a multivariate generalization of a procedure discussed by
Pearlman (1980) for calculating the exact likelihood of a univariate ARMA model. Ansley …

Three linear methods for estimating parameter values of vector auto‐regressive moving‐
average (VARMA) models which are in general at least an order of magnitude faster than …

The vector autoregressive model is very popular for modeling multiple time series.
Estimation of its parameters is typically done by a least squares procedure. However, this …

The problems of evaluating and subsequently maximizing the exact likelihood function of
vector autoregressive moving average (ARMA) models are considered separately. A new …

Dynamical characteristics of a complex system can often be inferred from analysis of a
stochastic time series model fitted to observations of the system. Oscillations in geophysical …

Time-varying statistics in linear filtering and linear estimation problems necessitate the use
of adaptive or time-varying filters in the solution. With the rapid availability of vast and …