GENERALIZED EMPIRICAL LIKELIHOOD INFERENCE FOR NONLINEAR AND TIME SERIES MODELS UNDER WEAK IDENTIFICATION

摘要

This paper studies robust inference methods for nonlinear moment restriction models with weakly identified parameters in time series contexts.Our methods are based on generalized empirical likelihood with kernel smoothing.The proposed test statistics,which follow the standard x~2 limiting distributions,are robust to weak identification and dependent data.