Regression and time series model selection in small samples

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ID: 289405
1989
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Abstract
A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. The correction is of particular use when the sample size is small, or when the number of fitted parameters is a moderate to large fraction of the sample size. The corrected method, called AICC, is asymptotically efficient if the true model is infinite dimensional. Furthermore, when the true model is of finite dimension, AICC is found to provide better model order choices than any other asymptotically efficient method. Applications to nonstationary autoregressive and mixed autoregressive moving average time series models are also discussed.
Reference Key
openalex_W1968371014 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Clifford M. Hurvich, Chih‐Ling Tsai
Journal jurnal biometrika dan kependudukan
Year 1989
DOI
10.1093/biomet/76.2.297
URL
https://doi.org/10.1093/biomet/76.2.297
Keywords Keywords not found

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