Model selection for integrated autoregressive processes of infinite order

Ching-Kang Ing; Chor-yiu Sin; Shu-Hui Yu

doi:10.1016/j.jmva.2011.10.008

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Journal article

Model selection for integrated autoregressive processes of infinite order

Ching-Kang Ing, Ching-Kang Ing, Chor-yiu Sin and Shu-Hui Yu

Journal of Multivariate Analysis, Vol.106, pp.57-71

04/2012

DOI: https://doi.org/10.1016/j.jmva.2011.10.008

Abstract

Asymptotic efficiency

Integrated AR() processes

Mean squared prediction error

Model selection

We show that Akaike's Information Criterion (AIC) and its variants are asymptotically efficient in integrated autoregressive processes of infinite order (AR(∞)). This result, together with its stationary counterpart established previously in the literature, ensures that AIC can ultimately achieve prediction efficiency in an AR(∞) process, without knowing the integration order. © 2011 Elsevier Inc.

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Title: Model selection for integrated autoregressive processes of infinite order
Creators - without role: Ching-Kang Ing - Institute of Statistical Science, Academia Sinica
Ching-Kang Ing - Institute of Statistical Science, Academia Sinica
Chor-yiu Sin - National Tsing Hua University
Shu-Hui Yu - National University of Kaohsiung
Publication Details: Journal of Multivariate Analysis, Vol.106, pp.57-71
Identifiers: 9957771328006774
Academic Unit: Department of Economics, College of Technology Management, National Tsing Hua University; Institute of Statistics and Data Science, College of Science, National Tsing Hua University
Language: English
Resource Type: Journal article

Model selection for integrated autoregressive processes of infinite order

Abstract

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