INVESTIGADORES
MONTES ROJAS Gabriel Victorio
artículos
Título:
A New Robust and Most Powerful Test in the Presence of Local Misspecification
Autor/es:
BERA, ANIL; MONTES ROJAS, GABRIEL VICTORIO; SOSA ESCUDERO, WALTER
Revista:
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
Editorial:
TAYLOR & FRANCIS INC
Referencias:
Lugar: Londres; Año: 2016
ISSN:
0361-0926
Resumen:
This paper proposes a new test that is consistent, achieves correct asymptotic size and is locally most powerful under local misspecification, and when any $sqrt n$-estimator of the nuisance parameters is used. The new test can be seen as an extension of the Bera and Yoon (1993) procedure that deals with non-ML estimation, while preserving its optimality properties. Similarly, the proposed test extends Neyman´s (1959) $C(alpha)$ test to handle locally misspecified alternatives. A Monte Carlo study investigates the finite sample performance in terms of size, power and robustness to misspecification.