Does the Bic Estimate and Forecast Better than the Aic?*
Keywords:
AIC, BIC, information criteria, time-series models, overfitting, forecast comparison, joint hypothesis testing
Abstract
We test two questions: (i) Is the Bayesian Information Criterion (BIC) more parsimonious than Akaike Information Criterion (AIC)? and (ii) Is BIC better than AIC for forecasting purposes? By using simulated data, we provide statistical inference of both hypotheses individually and then jointly with a multiple hypotheses testing procedure to control better for type-I error. Both testing procedures deliver the same result: The BIC shows an in- and out-of-sample superiority over AIC only in a long-sample context.
How to Cite
Medel, C. A., & Salgado, S. C. (1). Does the Bic Estimate and Forecast Better than the Aic?*. Economic Analysis Review, 28(1), 47-64. Retrieved from https://www.rae-ear.org/index.php/rae/article/view/rae-28-3
Issue
Section
Articles
Upon submission of an article, authors are asked to indicate their agreement to abide by an open-access license. The license permits any user to download, print out, extract, archive, and distribute the article, so long as appropriate credit is given to the authors of the work. The license ensures that your article will be as widely available as possible and that your article can be included in any scientific archive. Please read about the Creative Commons Attribution License before submitting your paper.
Except where otherwise noted, content on this site is licensed under a Creative Commons Attribution 3.0 License