Relevant Moment Selection under Mixed Identification Strength

主讲人: Firmin Doko Tchatoka

Dr. Firmin Doko Tchatoka is now an Associate Professor from School of Economics, The University of Adelaide (UoA). His research interest includes Econometrics, Statistics, Financial Econometrics, Networks econometrics and Big Data, Policy Evaluation Methods.

主持人: Andrew Pua

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) 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 (GMM) 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.


时间: 2021-05-08(Saturday)16:40-18:00
地点: Room N302, Economics Building
主办单位: 厦门大学经济学院、王亚南经济研究院
承办单位: 厦门大学经济学院、王亚南经济研究院
类型: 系列讲座