Relevant moment selection under mixed identification strength
Firmin Doko Tchatoka () and
Michael Aguessy ()
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Michael Aguessy: Economics Department, Concordia University
No 2019-04, School of Economics Working Papers from University of Adelaide, School of Economics
This paper proposes a moment selection method in the presence of moment condition models 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 sequentially evaluates the rate of convergence 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.
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