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.
Keywords
Akaike information criterionAutoregressive modelMathematicsSTAR modelSeries (stratigraphy)Model selectionSETARStatisticsSample size determinationAutoregressive integrated moving averageInformation CriteriaApplied mathematicsTime seriesDimension (graph theory)Regression analysisSelection (genetic algorithm)CombinatoricsComputer scienceArtificial intelligence
Affiliated Institutions
Related Publications
Model Selection and Akaike's Information Criterion (AIC): The General Theory and its Analytical Extensions
During the last fifteen years, Akaike's entropy-based Information Criterion (AIC) has had a fundamental impact in statistical model evaluation problems. This paper studies the g...
Introduction to Econometrics
Foreword. Preface to the Second Edition. Preface to the Third Edition. Obituary. INTRODUCTION AND THE LINEAR REGRESSION MODEL. What is Econometrics? Statistical Background and M...
Factor Analysis and AIC
The information criterion AIC was introduced to extend the method of maximum likelihood to the multimodel situation. It was obtained by relating the successful experience of the...
Comparative Performance of Bayesian and AIC-Based Measures of Phylogenetic Model Uncertainty
Reversible-jump Markov chain Monte Carlo (RJ-MCMC) is a technique for simultaneously evaluating multiple related (but not necessarily nested) statistical models that has recentl...
How Much Should We Trust Differences-In-Differences Estimates?
Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors...
Publication Info
- Year
- 1989
- Type
- article
- Volume
- 76
- Issue
- 2
- Pages
- 297-307
- Citations
- 6179
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
6179
OpenAlex
Cite This
Clifford M. Hurvich,
Chih‐Ling Tsai
(1989).
Regression and time series model selection in small samples.
Biometrika
, 76
(2)
, 297-307.
https://doi.org/10.1093/biomet/76.2.297
Identifiers
- DOI
- 10.1093/biomet/76.2.297