Abstract
Summary This paper proposes a bootstrap-based procedure for selecting the best moment conditions among a set of correctly specified moment conditions. The proposed method can be motivated by Edgeworth expansions and chooses moment conditions that minimize the approximate coverage error of confidence intervals and hypothesis tests for parameters. Because of the analytical intractability of Edgeworth expansions, we estimate the coverage error by the bootstrap. The proposed method can be applied to a wide class of estimators for possibly non-linear and possibly dynamic models. We investigate the small sample performance of the proposed method in Monte Carlo experiments.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
