We consider maximum likelihood estimation for both causal and noncausal autoregressive time series processes with non-Gaussian αstable noise. A nondegenerate limiting distribution is given for maximum likelihood estimators of the parameters of the autoregressive model equation and the parameters of the stable noise distribution. The estimators for the autoregressive parameters are n-consistent ...

Estimation of cointegrated systems via autoregressive approximation is considered in the framework developed by Saikkonen (1992). The asymptotic properties of the estimated coeecients of the autoregressive ECM (error correction model) and the pure VAR (vector autoregressive) representations are derived under the assumption that the autoregressive order goes to innnity with the sample size. Thes...

A methodology was developed for estimating the parameters involved in a first-order autoregressive process; these parameters comprise a variance component associated with the random effect, a correlation coefficient, p, and a residual variance. These parameters were estimated using REML with an expectationmaximization algorithm. For two singletrait analyses (milk and fat production being the de...

In this paper, we consider a flexible smooth transition autoregressive (STAR) model with multiple regimes and multiple transition variables. This formulation can be interpreted as a time varying linear model where the coefficients are the outputs of a single hidden layer feedforward neural network. This proposal has the major advantage of nesting several nonlinear models, such as, the Self-Exci...

High-resolution spectrum estimation techniques have been extensively studied in recent publications. Knowledge of the noise variance is vital for spectrum estimation from noise-corrupted observations. This paper presents the use of noise compensation and data extrapolation for spectrum estimation. We assume that the observed data sequence can be represented by a set of autoregressive parameters...

In this paper a signal modeling technique based upon finite mixture autoregressive probabilistic functions of Markov chains is developed and applied to the problem of speech recognition, particularly speaker-independent recognition of isolated digits. Two types of mixture probability densities are investigated: finite mixtures of Gaussian autoregressive densities (GAM) and nearest-neighbor part...

We propose a new class of observation driven time series models that we refer to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled likelihood score. This provides a unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models. The GAS model encompasses other well-known models such as the genera...

The power spectrum estimation for a multichannel autoregressive process using prewhitened and postcoloring technique, which was originally developed for a single channel, is proposed. In order to make the extension, the Cholesky decomposition of the inverse autocorrelation matrix for a multichannel autoregressive process is discussed and the autoregressive model order selection for a multichann...

We compare new time-series methods for estimating the death rate of an emerging and re-emerging disease and our approach is based on One-Dimensional Integrated Autoregressive Bilinear Time Series Model and Generalized Integrated Autoregressive Bilinear Time Series Model. The parameters of the proposed models are estimated using Newton-Raphson iterative method and statistical properties of the d...