Result: Robust GMM tests for structural breaks

Title:
Robust GMM tests for structural breaks
Authors:
GAGLIARDINI, Patrick, TROJANI, Fabio, URGA, Giovanni
Source:
Modelling structural breaks, long memory and stock market volatility: an overviewJournal of econometrics. 129(1-2):139-182
Publisher Information:
Amsterdam: Elsevier, 2005.
Publication Year:
2005
Physical Description:
print, 1 p.1/4
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Lois de probabilités, Distribution theory, Applications, Assurances, économie, finance, Insurance, economics, finance, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Analyse donnée, Data analysis, Análisis datos, Bootstrap, Donnée économique, Economic data, Dato económico, Econométrie, Econometrics, Econometría, Estimation moyenne, Mean estimation, Estimación promedio, Fonction influence, Influence function, Función influencia, Grand échantillon, Large sample, Modèle référence, Reference model, Modelo referencia, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode essai, Test method, Método ensayo, Méthode moment, Moment method, Método momento, Méthode statistique, Statistical method, Método estadístico, Petit échantillon, Small sample, Pequeña muestra, Robustesse estimateur, Estimator robustness, Robustez estimador, Robustesse test, Test robustness, Robustez prueba, Sensibilité, Sensitivity, Sensibilidad, Coupure structurale, Structural break, C10, C12, C13, C15 Robust tests, Generalized method of moment, Monte Carlo, Structural breaks
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Economics, Institute of Finance, University of Lugano, Via Buffi 13, Lugano 6900, Switzerland
Department of Economics, University of St. Gallen, Switzerland
Faculty of Finance, Cass Business School, 106 Bunhill Row, London EC1Y 8TZ, United Kingdom
ISSN:
0304-4076
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17222099
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.17222099
Database:
PASCAL Archive

Further Information

We propose a class of new robust Generalized Method of Moments (GMM) tests for endogenous structural breaks. The tests are based on supremum, average and exponential functionals derived from robust GMM estimators with bounded influence function. We study the theoretical local robustness properties of the new tests and show that they imply a uniformly bounded asymptotic sensitivity of size and power under general local deviations from a reference model. We then analyze the finite sample performance of the new robust tests via Monte Carlo simulations, and compare it with that of classical GMM tests for structural breaks. In large samples, we find that the performance of classical asymptotic GMM tests can be quite unstable under slight departures from some given reference distribution. In particular. the loss in power can be substantial in some models. Robust asymptotic tests for structural breaks yield important power improvements both in exactly identified and overidentified model settings. In small samples, bootstrapped versions of the classical and the robust GMM tests provide accurate and stable empirical levels also for quite small sample sizes. However, bootstrapped robust GMM tests are found to provide again a higher finite sample efficiency.