Treffer: MMC techniques for limited dependent variables models : Implementation by the branch-and-bound algorithm
Title:
MMC techniques for limited dependent variables models : Implementation by the branch-and-bound algorithm
Authors:
Source:
Annals journal of econometrics: Resampling methods in econometricsJournal of econometrics. 133(2):479-512
Publisher Information:
Amsterdam: Elsevier, 2006.
Publication Year:
2006
Physical Description:
print, 24 ref
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, Théorie de la décision, Decision 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, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Théorie de la décision. Théorie de l'utilité, Decision theory. Utility theory, Algorithme, Algorithm, Algoritmo, Donnée économique, Economic data, Dato económico, Econométrie, Econometrics, Econometría, Estimation statistique, Statistical estimation, Estimación estadística, Modèle logit, Logit model, Modelo logit, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode statistique, Statistical method, Método estadístico, Paramètre nuisance, Nuisance parameter, Parámetro daño, Problème mixte, Mixed problem, Problema mixto, Programmation en nombres entiers, Integer programming, Programación entera, Règle décision, Decision rule, Regla decisión, Statistique test, Test statistic, Estadística test, Echantillon fini, Finite sample, Méthode branch and bound, Région confiance, Confidence region, Test randomisé, Variable dépendante, Dependent variable, C12:C15:C24:C25;C44, Finite sample inference; Randomized tests; Maximized Monte Carlo tests; Branch-and-bound algorithm; Limited dependent variables model
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
GREMARS, Université Charles-de-Gaulle Lille 3, BP 60149, 59653 Villeneuve d'Ascq, France
CORE, Université Catholique de Louvain, Belgium
CORE, Université Catholique de Louvain, Belgium
ISSN:
0304-4076
Rights:
Copyright 2006 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
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
Operational research. Management
Operational research. Management
Accession Number:
edscal.18088586
Database:
PASCAL Archive
Weitere Informationen
We propose a finite sample approach to some of the most common limited dependent variables models. The method rests on the maximized Monte Carlo (MMC) test technique proposed by Dufour [1998. Monte Carlo tests with nuisance parameters: a general approach to finite-sample inference and nonstandard asymptotics. Journal of Econometrics, this issue]. We provide a general way for implementing tests and confidence regions. We show that the decision rule associated with a MMC test may be written as a Mixed Integer Programming problem. The branch-and-bound algorithm yields a global maximum in finite time. An appropriate choice of the statistic yields a consistent test, while fulfilling the level constraint for any sample size. The technique is illustrated with numerical data for the logit model.