Forecasting ARMA models: A comparative study of information criteria focusing on MDIC

Journal of Statistical Computation and Simulation

Volumn 80, Issue 1, 2010, Pages 61-73

  Mantalos, P ^a   Mattheou, K ^b   Karagrigoriou, A ^b  

^a LUND UNIVERSITY   (Sweden)

^b UNIVERSITY OF CYPRUS   (Cyprus)

Author keywords

ARMA process; Information criterion; MDIC; Model selection; NMSFE

Indexed keywords

EID: 77649139313     PISSN: 00949655     EISSN: 15635163     Source Type: Journal    
DOI: 10.1080/00949650802464137     Document Type: Article

References (29)

1. G.E.P. Box and G.M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, San Fransisco, 1970.
2. H. Akaike, Information theory and an extension of the maximum likelihood principle, in Proceedings of the 2nd International Symposium on Information Theory, B.N. Petrov and F. Csáki, eds.,Akademiai Kaido, Budapest, 1973, pp. 267-281.
3. G. Schwarz, Estimating the dimension of a model, Ann. Statist. 6 (1978), pp. 461-464.
4. E.J. Hannan and B.G. Quinn, The determination of the order of an autoregression, J. R. Statist. Soc. B 41 (1979), pp. 190-195.
5. P. Mantalos, K. Mattheou, and A. Karagrigoriou, Using the divergence information criterion for the determination of the order of an autoregressive process, TR 5/2008, Department of Mathematics and Statistics, University of Cyprus, 2008.
6. K. Mattheou, On new developments in statistical inference for measures of divergence, Ph.D. thesis, Department of Mathematics and Statistics, University of Cyprus, 2007.
7. K. Mattheou, S. Lee, and A. Karagrigoriou, A model selection criterion based on the BHHJ measure of divergence, J. Statist. Plann. Inferen. (2008), http://dx.doi.org/10.1016/j.jspi.2008.04.022.
8. H. Akaike, Fitting autoregression for predictions, Ann. Instit. Statist. Math. 21 (1969), pp. 243-247.
9. C.M. Hurvich and C.L. Tsai, Regression and time series model selection in small samples, Biometrika 76 (1989), pp. 297-307.
10. S. Kullback and R. Leibler, On information and sufficiency, Ann. Math. Statist. 22 (1951), pp. 79-86.
11. E.J. Hannan, The estimation of the order of an ARMA process, Ann. Statist. 8 (1980), pp. 1071-1081.
12. R. Shibata, Asymptotically efficient selection of the order of the model for estimating parameters of linear process, Ann. Statist. 8 (1980), pp. 147-164.
13. R. Shibata, An optimal selection of regression variables, Biometrika 68 (1981), pp. 45-54.
14. R.J. Bhansali, Asymptotically efficient selection of the order by the criterion autoregressive transfer function, Ann. Statist. 14 (1986), pp. 315-325.
15. A. Karagrigoriou, Asymptotic efficiency of the order selection of a non-Gaussian AR process, Statist. Sinica 7 (1997), pp. 407-423.
16. G. Claeskens and N.L. Hjort, The focused information criterion (with discussion) JASA 98 (2003), pp. 900-916.
17. B. Liquet, C. Sakarovitch, and D. Commenges, Bootstrap choice of estimators in parametric and semiparametric families: an extension of EIC, Biometrics 59 (2003), pp. 172-178.
18. M. Ishiguro, Y. Sakamoto, and G. Kitagawa, Bootstrapping log likelihood and EIC, an extension of AIC, Ann. Inst. Statist. Math. 49 (1997), pp. 411-434.
19. S. Lee and A. Karagrigoriou, An asymptotically optimal selection of the order of a linear process, Sankhya A 63(1) (2001), pp. 93-106.
20. J. Shang and J.E. Cavanaugh, Bootstrap variants of the Akaike information criterion for mixed model selection, Comput. Statist. Data Anal. 52 (2008), pp. 2004-2021.
21. J. Shang, Selection criteria based on Monte Carlo simulation and cross validation in mixed models, Far East J. Theor. Statist. 25 (2008), pp. 51-72.
22. C. Koukouvinos, K. Mylona, and F. Vonta, A comparative study of variable selection procedures applied in high dimensional medical problems, J. Appl. Prob. Statist. 3(2) (2008), pp. 195-209.
23. C. Koukouvinos and K. Mylona, A method for analyzing supersaturated designs with block orthogonal structure, Commun. Statist.- Simul. Comput. 37 (2008), pp. 290-300.
24. J.E. Cavanaugh and W.O. Johnson, Assessing the predictive influence of cases in a state-space process, Biometrika 86 (1999), pp. 183-190.
25. T. Bengtsson and J.E. Cavanaugh, An improved Akaike information criterion for state-space model selection, Comput. Statist. Data Anal. 50 (2006), pp. 2635-2654.
26. S. Lee, On the quantile process based on the autoregressive residuals, J. Statist. Plann. Inferen. 67 (1998), pp. 17-28.
27. Y.L. Choi, Sungim, and S. Lee, Generalized linear model with time series data, Kor. J. Appl. Statist. 16 (2) (2003), pp. 365-376.
28. A. Basu, I.R. Harris, N.L. Hjort, and M.C. Jones, Robust and efficient estimation by minimising a density power divergence, Biometrika 85 (1998), pp. 549-559.
29. H. Lütkepohl, New Introduction to Multiple Time-Series Analysis, Springer-Verlag, Berlin, Heidelberg, 2005.