On a method of identification of best subset model from full ar-model

An usual approach for selection of the best subset AR model of known maximal order is to use an appropriate information criterion, like AIC or SIC with an exhaustive selection of regressors and to choose the subset model that produces the optimum (minimum) value of AIC or SIC. This method is computationally intensive. Proposed is a method based on the use of singular value decomposition and QR with column pivoting factorization for extracting a reduced subset from the exhaustive candidate set of regressors and to use AIC or SIC on the reduced subset to obtain the best subset AR model. The result is substantially reduced domain of exhaustive search for the computation of the best subset AR model.