Asymptotic variance approximations for invariant estimators in uncertain asset-pricing models
推导了最大似然和连续更新GMM估计量在模型可能不满足资产定价约束时的渐近方差表达式，允许模型被数据拒绝时仍进行有效推断，并通过模拟和实证展示了误设修正的重要性。
This article derives explicit expressions for the asymptotic variances of the maximum likelihood and continuously-updated GMM estimators in models that may not satisfy the fundamental asset-pricing restrictions in population. The proposed misspecification-robust variance estimators allow the researcher to conduct valid inference on the model parameters even when the model is rejected by the data. While the results for the maximum likelihood estimator are only applicable to linear asset-pricing models, the asymptotic distribution of the continuously-updated GMM estimator is derived for general, possibly nonlinear, models. The large corrections in the asymptotic variances, that arise from explicitly incorporating model misspecification in the analysis, are illustrated using simulations and an empirical application.