TY - JOUR
T1 - Computational aspects of maximum likelihood of autoregressive fractionally integrated moving average models
AU - Ooms, M.
AU - Doornik, J.A.
PY - 2003
Y1 - 2003
N2 - Computational aspects of likelihood-based estimation of univariate ARFIMA (p,d,q) models are addressed. Particular issues are the numerically stable evaluation of the autocovariances and efficient handling of the variance matrix which has dimension equal to the sample size. It is shown how efficient computation and simulation are feasible, even for large samples. Implementation of analytical bias corrections in ARFIMA regression models is also discussed. © 2002 Elsevier Science B.V. All rights reserved.
AB - Computational aspects of likelihood-based estimation of univariate ARFIMA (p,d,q) models are addressed. Particular issues are the numerically stable evaluation of the autocovariances and efficient handling of the variance matrix which has dimension equal to the sample size. It is shown how efficient computation and simulation are feasible, even for large samples. Implementation of analytical bias corrections in ARFIMA regression models is also discussed. © 2002 Elsevier Science B.V. All rights reserved.
U2 - 10.1016/S0167-9473(02)00212-8
DO - 10.1016/S0167-9473(02)00212-8
M3 - Article
SN - 0167-9473
VL - 42
SP - 333
EP - 348
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
IS - 3
ER -