AutoregressiveModel2CovarianceMatrix

This program computes the covariance structure of a random process represented by an AR model sequence. The covariance matrix is determined by accumulating the normal equations of all AR models in autoregressiveModelSequence and inverting the combined normal equation matrix. For each output file in outputfileCovarianceMatrix, the covariance matrix of appropriate time lag is saved (the first file contains the auto-covariance, second file cross covariance and so on). The matrix for lag $h$ describes the covariance between $x_{t-h}$ and $x_{t}$, i.e. $\Sigma(t-h, t)$.