var.cov.matrix

<p>Selects an optimal ARMA model (according to an information criteria) for the time series in the data vector, <code>x</code>; then, fits such model and analyses the corresponding residuals. If the ARMA model is suitable, returns the <code>n x n</code> variance-covariance matrix corresponding to <code>n</code> consecutive variables in the ARMA process. If the ARMA model is not suitable, it informs the user with a message.</p>

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Contains statistical inference tools applied to Partial Linear Regression (PLR) models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of covariance are considered. Kernel-based methods, combined with ordinary least squares estimation, are used and time series errors are allowed. In addition, these techniques are also implemented for both parametric (linear) and nonparametric regression models.

Ana L<c3><b3>pez-Cheda

var.cov.matrix: Estimated variance-covariance matrix from time series

Description

Usage

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