SMOOTH MINIMUM DISTANCE ESTIMATION AND TESTING IN

UNIVERSITE LIBRE DE BRUXELLES
DEPARTEMENT DE MATHEMATIQUE
INSTITUT DE RECHERCHE EN STATISTIQUE (ECARES)
SEMINAIRE
SMOOTH MINIMUM DISTANCE ESTIMATION
AND TESTING IN CONDITIONAL MOMENT
RESTRICTIONS MODELS :
UNIFORM IN BANDWIDTH THEORY.
Pascal LAVERGNE
Simon Fraser University and CREST-ENSAI
(joint work with Valentin PATILEA, INSA-IRMAR and CREST-ENSAI)
VENDREDI 6 MARS 2009 à 14 H 30
Campus Plaine – Bâtiment NO – 9ème étage – Salle des Professeurs
ABSTRACT
We propose a new class of estimators for models de ned by conditional moment restrictions. Our
generic estimator minimizes a distance criterion based on kernel smoothing. We develop a
theory that focuses on uniformity in bandwidth. We establish a pnasymptotic representation of
our estimator as a process depending on the bandwidth within a wide range including xed
bandwidths and that applies to misspecied models. We also study an ecient version of our
estimator. We develop inference procedures based on a distance metric statistic for testing
restrictions on parameters and we propose a new bootstrap technique. Our new methods apply
to non-smooth problems, are simple to implement, and perform well in small samples
2008-2009/14