New penalty in information criteria for the ARCH sequence with structural changes

Abstract

For change point models and autoregressive conditional heteroscedasticity (ARCH) models, which have long been important especially in econometrics, we develop information criteria that work well even when considering a combination of these models. Since the change point model does not satisfy the conventional statistical asymptotics, a formal Akaike information criterion (AIC) with twice the number of parameters as the penalty term would clearly result in overfitting. Therefore, we derive an AIC‐type information criterion from its original definition using asymptotics peculiar to the change point model. Specifically, we suppose time series data treated in econometrics and derive Takeuchi information criterion (TIC) as the main information criterion allowing for model misspecification. It is confirmed that the penalty for the change point parameter is almost three times larger than the penalty for the regular parameter. We also derive the AIC in this setting from the TIC by removing the consideration of the model misspecification. In numerical experiments, the derived TIC and AIC are compared with the formal AIC and Bayesian information criterion (BIC). It is shown that the derived information criteria clearly outperform the others in light of the original purpose of AIC, which is to give an estimate close to the true structure. We also ensure that the TIC seems to be superior to the AIC in the presence of model misspecification.