A bootstrap approach to evaluating the performance of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) in selection of an asymmetric price relationship
2012
Acquah Henry de-Graft
This study addresses the problem of model selection in asymmetric price transmission models by combining the use of bootstrap methods with information theoretic selection criteria. Subsequently, parametric bootstrap technique is used to select the best model according to Akaike’s Information Criteria (AIC) and Bayesian Information Criteria (BIC). Bootstrap simulation results indicated that the performances of AIC and BIC are affected by the size of the data, the level of asymmetry and the amount of noise in the model used in the application. This study further establishes that the BIC is consistent and outperforms AIC in selecting the correct asymmetric price relationship when the bootstrap sample size is large.
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