var.cov.sum

<p>For each time series in the columns of the data matrix, <code>X</code>, selects an optimal ARMA model (according to an information criteria); then, fits such model and analyses the corresponding residuals. If all the ARMA models are suitable, returns a vector containing the corresponding sums the autocovariances. If some 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.sum: Estimated sum of autocovariances from time series

Description

Usage

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