Result: A parametric bootstrap test for cycles

Title:
A parametric bootstrap test for cycles
Authors:
DALLA, Violetta, HIDALGO, Javier
Source:
Modelling structural breaks, long memory and stock market volatility: an overviewJournal of econometrics. 129(1-2):219-261
Publisher Information:
Amsterdam: Elsevier, 2005.
Publication Year:
2005
Physical Description:
print, 1 p.3/4
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, Inférence à partir de processus stochastiques; analyse des séries temporelles, Inference from stochastic processes; time series analysis, 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 covariance, Covariance analysis, Análisis covariancia, Bootstrap, Densité spectrale, Spectral density, Densidad espectral, Econométrie, Econometrics, Econometría, Fonction densité, Density function, Función densidad, Loi limite, Limit distribution, Ley límite, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode domaine temps, Time domain method, Método dominio tiempo, Méthode statistique, Statistical method, Método estadístico, Processus Gauss, Gaussian process, Proceso Gauss, Processus linéaire, Linear process, Proceso lineal, Test hypothèse, Hypothesis test, Test hipótesis, Test paramétrique, Parametric test, Prueba paramétrica, Valeur critique, Critical value, Valor crítico, Donnée cyclique, Cyclical data, Dépendance faible, Dépendence forte, Strong dependence, Estimateur Whittle, Test linéaire, Linear test, Bootstrap algorithms, C15, C22 Cyclical data, Spectral density function, Strong and weak dependence, Whittle estimator
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Economics Department, London School of Economics, Houghton Street, London WC2A 2AE, United Kingdom
ISSN:
0304-4076
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17222101
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.17222101
Database:
PASCAL Archive

Further Information

The paper proposes a simple test for the hypothesis of strong cycles and as a by-product a test for weak dependence for linear processes. We show that the limit distribution of the test is the maximum of a (semi) Gaussian process G(τ), τ ∈ [0,1]. Because the covariance structure of G(τ) is a complicated function of T and model dependent, to obtain the critical values (if possible) of maxτ∈[0,1] G(τ) may be difficult. For this reason, we propose a bootstrap scheme in the frequency domain to circumvent the problem of obtaining (asymptotically) valid critical values. The proposed bootstrap can be regarded as an alternative procedure to existing bootstrap methods in the time domain such as the residual-based bootstrap. Finally, we illustrate the performance of the bootstrap test by a small Monte-Carlo experiment and an empirical example.