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非平稳条件下的模型选择：自回归与随机线性回归模型AI 解读

We give sufficient conditions for strong consistency of estimators for the order of general nonstationary autoregressive models based on the minimization of an information criterion a la Akaike's (1969) AIC. The case of a time-dependent error variance is also covered by the analysis. Furthermore, the more general case of regressor selection in stochastic regression models is treated.

利用拉格朗日乘数检验估计ARMA模型阶数AI 解读

A stepwise testing procedure using Lagrangian multiplier tests is developed to determine the order of an ARMA-process. The strong consistency of this procedure under slightly weaker assumptions than in Hannan (1980) (proof of the consistency of the order estimators obtained via BIC) is proved.