Relevant moment selection under mixed identification strength

Speaker: Professor Firmin Doko Tchatoka

Affiliation: University of Adelaide

Online via Zoom: https://uqz.zoom.us/j/86194693356

Abstract

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.

About the presenter's meeting

Prof. Tchatoka is willing to have (30-min) 1-to-1 meeting with our colleagues and graduate students on the seminar day. Anyone who wants to schedule a meeting with him can contact Dr Fu Ouyang to book a time slot.