Result: Diagnostics for conditional heteroscedasticity models: some simulation results
Title:
Diagnostics for conditional heteroscedasticity models: some simulation results
Authors:
Source:
Selected Papers of the MSSANZ/IMACS 14th Biennal Conference on Modelling and SimulationMathematics and computers in simulation. 64(1):113-119
Publisher Information:
Amsterdam: Elsevier, 2004.
Publication Year:
2004
Physical Description:
print, 11 ref
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Computer science, Informatique, Mathematics, Mathématiques, Mechanics acoustics, Mécanique et acoustique, 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, Inférence linéaire, régression, Linear inference, regression, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Analyse numérique, Numerical analysis, Análisis numérico, Diagnostic, Diagnosis, Diagnóstico, Hétéroscedasticité, Heteroscedasticity, Heteroscedasticidad, Loi conditionnelle, Conditional distribution, Ley condicional, Maximum vraisemblance, Maximum likelihood, Maxima verosimilitud, Modèle simulation, Simulation model, Modelo simulación, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode statistique, Statistical method, Método estadístico, Méthode stochastique, Stochastic method, Método estocástico, Simulation, Simulación, Test statistique, Statistical test, Test estadístico, Variance, Variancia, 65C05, Test Li Mak, Li Mak test, Test Wooldridge, Wooldridge test
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Economics, National University of Singapore, AS2, Arts Link, Kent Ridge Crescent, Singapore 119260, Singapore
ISSN:
0378-4754
Rights:
Copyright 2004 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
Accession Number:
edscal.15358508
Database:
PASCAL Archive
Further Information
In this paper, we study the size and power of various diagnostic statistics for univariate conditional heteroscedasticity models. These test statistics include the residual-based tests recently derived by Tse, Li and Mak, and Wooldridge, respectively. Monte-Carlo experiments with 1000 replications are conducted to generate conditional variances which follow the autoregressive conditional heteroscedasticity (ARCH)/GARCH processes. We use quasi-maximum likelihood estimation (MLE) method to obtain estimates of parameters under different ARCH/ generalized ARCH (GARCH) models. It is found that the Tse and Li-Mak diagnostics are more powerful.