A new robust and most powerful test in the presence of local misspecification

Anil K. Bera, Gabriel Montes-Rojas, Walter Sosa-Escudero

Research output: Contribution to journalArticlepeer-review

Abstract

This article proposes a new test that is consistent, achieves correct asymptotic size, and is locally most powerful under local misspecification, and when any √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 maximum likelihood (ML) estimation, while preserving its optimality properties. Similarly, the proposed test extends Neyman's (1959) C(α) test to handle locally misspecified alternatives. A Monte Carlo study investigates the finite sample performance in terms of size, power, and robustness to misspecification.

Original languageEnglish (US)
Pages (from-to)8187-8198
Number of pages12
JournalCommunications in Statistics - Theory and Methods
Volume46
Issue number16
DOIs
StatePublished - Aug 18 2017

Keywords

  • Local misspecification
  • Neyman's C(α)
  • Rao's score test
  • specification testing

ASJC Scopus subject areas

  • Statistics and Probability

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