- Volume 3 Issue 3
A Comparative Study of the GPAC Method and the 3-pattern Method for Identifying ARMA Processes
- Chul Eung KIM (Assistant Professor, Department of Applied Statistics, Yonsei University, Seoul 120-749, Korea) ;
- ByoungSeon CHOI (Professor, Department of Applied Statistics, Yonsei University, Seoul, 120-749, Korea)
- Published : 1996.12.01
The generalized partial autocorrelation (GPAC) method of Woodward and Gray (1981) and the 3-pattern method of Choi (1991) have been used for identifying ARMA processes. The methods are based on the extended Yule-Walker equations. The purpose of this paper is to show the 3-pattern method is superior to the GPAC method through theoretical analysis and computer simulations.
- Journal of Time Series Analysis v.12 On the asymptotic distribution of the generalized partial autocorrelation function in sutoregressive moving-average processes Choi, B. S.
- ARMA Model Identification Choi, B. S.
- IEEE Transactions on Signal Processing v.41 Two chi-square statistics for determining the orders p and q of an ARMA (p,q) process Choi, B. S.
- Journal of the Japan Statistical Society v.24 no.1 The asymptotic distributions of the θ, λ and η function estimates for identifying a mixed ARMA process Choi, B. S.
- Journal of the American Statistical Association v.79 On the use of the general partial autocorrelation function for order determination in ARMA(p,q) processes Davies, N.;J. D. Petruccelli
- Journal of Time Series Analysis v.2 Some aspects of modeling and forecasting multivariate time series Jenkins. G. M.;A. S. Alavi
- Journal of Time Series Analysis v.4 On q-conditioned partial correlations Newbold, P.;T. Bos
- Journal of the American Statistical Association v.76 On the relationship between the s array and the Box-Jenkins method of ARMA model identification Woodward;W. A.;H. L. Gray