[en] Previous studies have shown that existing moment-based estimation approaches have poor small-sample performance in some applications. We propose an alternative that is based on the ESP (empirical saddlepoint) approximation of the solutions to the empirical moment conditions. Saddlepoint approximations are known to perform well in small sample. The novel estimator proposed, which we call the ESP estimator, is the mode of the ESP approximation. We show that it is consistent and asymptotically normal, and we study its higher-order bias. We propose novel test statistics based on the ESP estimator. Finally, we also investigate the finite-sample properties of the ESP estimator and related test statistics through Monte-Carlo simulations.
Disciplines :
Méthodes quantitatives en économie & gestion
Auteur, co-auteur :
HOLCBLAT, Benjamin ; University of Luxembourg > Faculty of Law, Economics and Finance (FDEF) > Luxembourg School of Finance (LSF)
Sowell, Fallaw; Carnegie Mellon University > Tepper Business School
CFEnetwork, Birkbeck University of London and King's College London
Lieu de la manifestation :
London, Royaume-Uni
Date de la manifestation :
16-18 December 2017
Manifestation à portée :
International
Références de l'abstract :
Previous studies have shown that existing moment-based estimation approaches have poor small-sample performance in some applications. We propose an alternative that is based on the ESP (empirical saddlepoint) approximation of the solutions to the empirical moment conditions. Saddlepoint approximations are known to perform well in small sample. The novel estimator proposed, which we call the ESP estimator, is the mode of the ESP approximation. We show that it is consistent and asymptotically normal, and we study its higher-order bias. We propose novel test statistics based on the ESP estimator. Finally, we also investigate the finite-sample properties of the ESP estimator and related test statistics through Monte-Carlo simulations.
