The most commonly used method for estimating the time domain parameters of an autoregressive process is to use the Yule-Walker equations. The Yule-Walker estimates of the parameters of an autoregressive process are known to often be highly biased. There is a Fourier transform relationship between the autocovariance sequence for an autoregressive process (the estimates of which are used in the Y...

In this paper we present a Feed-Foward Neural Networks Autoregressive (FFNN-AR) model with genetic algorithms training optimization in order to predict the gross domestic product growth of six countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from inpu...

The first–order integer–valued autoregressive (INAR(1)) process is investigated, where the autoregressive coefficient is close to one. It is shown that the limiting distribution of the conditional least–squares estimator for this coefficient is normal and, in contrast to the familiar AR(1) process, the rate of convergence is n. Finally, the nearly critical Galton–Watson process with unobservabl...

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...

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...