ON THE RECURSIVE FITTING OF SUBSET AUTOREGRESSIONS
- 1 January 1982
- journal article
- Published by Wiley in Journal of Time Series Analysis
- Vol. 3 (1) , 43-59
- https://doi.org/10.1111/j.1467-9892.1982.tb00329.x
Abstract
In fitting a vector autoregressive process which may include lags up to and including lagK, we may wish to search for the subset vector autoregressive process of sizek(wherekis the number of lags with non‐zero coefficient matrices,k= 1, 2,K) which has the minimum generalized residual variance. This paper provides a recursive procedure, which is initialized by evaluatingall‘forwardand’‘backward’ autoregressions in whichk= 1. The recursion then allows one to develop successively all subsets of sizek= 2,k= 3 up tok=K.The optimum subset vector autoregression is found by employing the proposed recursive procedures in conjunction with model selection criteria. This approach is used on simulated data to assess its performance and to re‐examine the annual trappings of the Canadian lynx investigated by Tong (1977).Keywords
This publication has 10 references indexed in Scilit:
- Estimating the dimension of a linear systemJournal of Multivariate Analysis, 1981
- The Determination of the Order of an AutoregressionJournal of the Royal Statistical Society Series B: Statistical Methodology, 1979
- Estimating the Dimension of a ModelThe Annals of Statistics, 1978
- Some Comments on the Canadian Lynx DataJournal of the Royal Statistical Society. Series A (General), 1977
- Subset AutoregressionTechnometrics, 1975
- Computational Efficiency in the Selection of Regression VariablesTechnometrics, 1970
- Fitting autoregressive models for predictionAnnals of the Institute of Statistical Mathematics, 1969
- Selection of the Best Subset in Regression AnalysisTechnometrics, 1967
- Selection of the Best Subset in Regression AnalysisTechnometrics, 1967
- On the fitting of multivariate autoregressions, and the approximate canonical factorization of a spectral density matrixBiometrika, 1963