Result: A consistent bootstrap test for conditional density functions with time-series data
Title:
A consistent bootstrap test for conditional density functions with time-series data
Authors:
Source:
Annals journal of econometrics: Resampling methods in econometricsJournal of econometrics. 133(2):863-886
Publisher Information:
Amsterdam: Elsevier, 2006.
Publication Year:
2006
Physical Description:
print, 28 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, 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, Bootstrap, Donnée économique, Economic data, Dato económico, Econométrie, Econometrics, Econometría, Estimation statistique, Statistical estimation, Estimación estadística, Fonction densité, Density function, Función densidad, Loi conditionnelle, Conditional distribution, Ley condicional, Méthode statistique, Statistical method, Método estadístico, Normalité asymptotique, Asymptotic normality, Normalidad asintótica, Statistique test, Test statistic, Estadística test, Série temporelle, Time series, Serie temporal, Test hypothèse, Hypothesis test, Test hipótesis, Théorie prévision, Forecasting theory, Loi asymptotique, Bootstrap; Conditional density function; Density forecasting, C12; C15; E37
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Monetary and Financial Analysis, Bank of Canada, 234 Wellington Street, Ottawa, Ontario, K1A 0G9, Canada
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
Accession Number:
edscal.18088599
Database:
PASCAL Archive
Further Information
This paper presents a new test for evaluating conditional density functions for time-series data, thereby being applicable to forecasting problems. We show that the test statistic is asymptotically distributed standard normal under the null hypothesis, and diverges to infinity when the null hypothesis is false. We use a bootstrap algorithm to approximate the distribution of the test statistic, and show that the bootstrap distribution converges to the asymptotic distribution of the test statistic in probability. An application to inflation forecasting is also presented to demonstrate the usefulness of the test.