Maximum Likelihood Estimation of Misspecified Models
研究使用最大似然技术进行估计和推断时模型误设的后果与检测，指出准最大似然估计量收敛于某个极限但可能不一致，标准检验失效，并给出稳健推断和误设检验的方法。
This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference. The quasi-maximum likelihood estimator (QMLE) converges to a well defined limit, and may or may not be consistent for particular parameters of interest. Standard tests (Wald, Lagrange Multiplier, or Likelihood Ratio) are invalid in the presence of misspecification, but more general statistics are given which allow inferences to be drawn robustly. The properties of the QMLE and the information matrix are exploited to yield several useful tests for model misspecification.