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RELEVANT MOMENT SELECTION UNDER MIXED IDENTIFICATION STRENGTH

Published online by Cambridge University Press:  16 January 2023

Prosper Dovonon ,
Firmin Doko Tchatoka  and
Michael Aguessy
Prosper Dovonon*
Affiliation:
Department of Economics, Concordia University
Firmin Doko Tchatoka
Affiliation:
School of Economics and Public Policy, The University of Adelaide
Michael Aguessy
Affiliation:
Department of Economics, Concordia University
*
Address correspondence to Prosper Dovonon, Department of Economics, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, QC H3G 1M8, Canada; e-mail: prosper.dovonon@concordia.ca.
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Abstract

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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.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 40 , Issue 5 , October 2024 , pp. 1003 - 1064
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Footnotes

The paper has benefited from many comments of the Co-Editor (Michael Jansson), the Editor (Peter Phillips), and two anonymous referees. We thank Jean-Jacques Forneron, Christian Gouriéroux, Zhongjun Qu, Eric Renault, Rami Tabri, Brendan K. Beare, and Ye Lu for helpful comments. We also thank the participants of the 2018 Africa Meeting of the Econometric Society in Benin and the 2018 Canadian Econometric Study Group meeting in Ottawa, and seminar participants at Boston University, York University, and the University of Sydney for helpful discussions. This research is supported by the Social Sciences and Humanities Research Council of Canada and by the Australian Research Council grant DP200101498.

References

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