Invites you to a

Econometrics seminar presented by

Firmin Doko Tchatoka

(University of Adelaide)

Relevant moment selection under mixed identification strength

Wednesday 7 September

2.00pm – 3.30pm

Via Zoom: Meeting Link

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.

For further information contact: Econometrics seminar series coordinator Dr Ye Lu (ye.lu1@sydney.edu.au)

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