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AIC and BIC

Comparisons of Assumptions and Performance

London School of Economics

The two most commonly used penalized model selection criteria, the Bayesian information criterion (BIC) and Akaike’s information criterion (AIC), are examined and compared. Their motivations as approximations of two different target quantities are discussed, and their performance in estimating those quantities is assessed. Despite their different foundations, some similarities between the two statistics can be observed, for example, in analogous interpretations of their penalty terms. The behavior of the criteria in selecting good models for observed data is examined with simulated data and also illustrated with the analysis of two well-known data sets on social mobility. It is argued that useful information for model selection can be obtained from using AIC and BIC together, particularly from trying as far as possible to find models favored by both criteria.

Key Words: Bayesian inference • Kullback-Leibler divergence • mobility tables • model selection • parsimony • prediction

Sociological Methods & Research, Vol. 33, No. 2, 188-229 (2004)
DOI: 10.1177/0049124103262065


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