关键词: STAR模型, 信息准则, 滞后阶数选择

Abstract: This paper discusses six information criteria of the applicability and robustness for determining the lag order of smooth transition autoregressive (STAR) models via Monte Carlo simulations. Simulation results show that in most situations, the error distribution of the data generation process does not influence the ability of the information criteria to correctly recognize the maximum lag order of a STAR model. For a short STAR model (a model with a lag order <5), the ACC criterion can determine the actual maximum lag order with higher accuracy, and exhibits robustness to different smooth transition coefficients and threshold values. The same results are generated by the Schwarz and ACC criteria for a long STAR model (lag order >5).

Key words: STAR Models, Information Criterion, Lag Order Selection