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An Improved Divergence Information Criterion for the Determination of the Order of an AR Process

Mantalos, Panagiotis LU ; Mattheou, K. and Karagrigoriou, A. (2010) In Communications in Statistics: Simulation and Computation 39(5). p.865-879
Abstract
In this article we propose a modification of the recently introduced divergence information criterion (DIC, Mattheou et al., 2009) for the determination of the order of an autoregressive process and show that it is an asymptotically unbiased estimator of the expected overall discrepancy, a nonnegative quantity that measures the distance between the true unknown model and a fitted approximating model. Further, we use Monte Carlo methods and various data generating processes for small, medium, and large sample sizes in order to explore the capabilities of the new criterion in selecting the optimal order in autoregressive processes and in general in a time series context. The new criterion shows remarkably good results by choosing the correct... (More)
In this article we propose a modification of the recently introduced divergence information criterion (DIC, Mattheou et al., 2009) for the determination of the order of an autoregressive process and show that it is an asymptotically unbiased estimator of the expected overall discrepancy, a nonnegative quantity that measures the distance between the true unknown model and a fitted approximating model. Further, we use Monte Carlo methods and various data generating processes for small, medium, and large sample sizes in order to explore the capabilities of the new criterion in selecting the optimal order in autoregressive processes and in general in a time series context. The new criterion shows remarkably good results by choosing the correct model more frequently than traditional information criteria. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Model, Measure of divergence, AR process, Information criterion, selection
in
Communications in Statistics: Simulation and Computation
volume
39
issue
5
pages
865 - 879
publisher
Taylor & Francis
external identifiers
  • wos:000277568500001
  • scopus:77952391452
ISSN
0361-0918
DOI
10.1080/03610911003650391
language
English
LU publication?
yes
id
2f7bd871-9fdd-478e-95cc-73d22e3f562b (old id 1618001)
date added to LUP
2010-06-21 08:44:54
date last changed
2018-05-29 11:07:10
@article{2f7bd871-9fdd-478e-95cc-73d22e3f562b,
  abstract     = {In this article we propose a modification of the recently introduced divergence information criterion (DIC, Mattheou et al., 2009) for the determination of the order of an autoregressive process and show that it is an asymptotically unbiased estimator of the expected overall discrepancy, a nonnegative quantity that measures the distance between the true unknown model and a fitted approximating model. Further, we use Monte Carlo methods and various data generating processes for small, medium, and large sample sizes in order to explore the capabilities of the new criterion in selecting the optimal order in autoregressive processes and in general in a time series context. The new criterion shows remarkably good results by choosing the correct model more frequently than traditional information criteria.},
  author       = {Mantalos, Panagiotis and Mattheou, K. and Karagrigoriou, A.},
  issn         = {0361-0918},
  keyword      = {Model,Measure of divergence,AR process,Information criterion,selection},
  language     = {eng},
  number       = {5},
  pages        = {865--879},
  publisher    = {Taylor & Francis},
  series       = {Communications in Statistics: Simulation and Computation},
  title        = {An Improved Divergence Information Criterion for the Determination of the Order of an AR Process},
  url          = {http://dx.doi.org/10.1080/03610911003650391},
  volume       = {39},
  year         = {2010},
}