RELEVANT MOMENT SELECTION UNDER MIXED IDENTIFICATION STRENGTH
针对矩条件模型中识别强度混合（部分矩函数在参数集上一致趋近于零）导致现有选择方法不一致的问题，提出一种新准则，自动考虑候选模型参数估计的收敛速度和渐近分布的熵，并通过蒙特卡洛模拟验证有限样本表现。
This paper proposes a robust moment selection method aiming to pick the best model even if this is a moment condition model with mixed identification strength, that is, moment conditions including moment functions that are local to zero uniformly over the parameter set. We show that the relevant moment selection procedure of Hall et al. (2007, Journal of Econometrics 138, 488–512) is inconsistent in this setting as it does not explicitly account for the rate of convergence of parameter estimation of the candidate models which may vary. We introduce a new moment selection procedure based on a criterion that automatically accounts for both the convergence rate of the candidate model’s parameter estimate and the entropy of the estimator’s asymptotic distribution. The benchmark estimator that we consider is the two-step efficient generalized method of moments estimator, which is known to be efficient in this framework as well. A family of penalization functions is introduced that guarantees the consistency of the selection procedure. The finite-sample performance of the proposed method is assessed through Monte Carlo simulations.