Result: Small sample properties of forecasts from autoregressive models under structural breaks

Title:
Small sample properties of forecasts from autoregressive models under structural breaks
Authors:
PESARAN, M. Hashem, TIMMERMANN, Allan
Source:
Modelling structural breaks, long memory and stock market volatility: an overviewJournal of econometrics. 129(1-2):183-217
Publisher Information:
Amsterdam: Elsevier, 2005.
Publication Year:
2005
Physical Description:
print, 1 p.1/2
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 donnée, Data analysis, Análisis datos, Donnée économique, Economic data, Dato económico, Econométrie, Econometrics, Econometría, Erreur moyenne, Mean error, Error medio, Estimation erreur, Error estimation, Estimación error, Estimation sans biais, Unbiased estimation, Estimación insesgada, Macroéconomie, Macroeconomics, Macroeconomía, Modèle autorégressif, Autoregressive model, Modelo autorregresivo, Modèle prévision, Forecast model, Modelo previsión, Modèle structure, Structural model, Modelo estructura, Modèle théorique, Theoretical model, Modelo teórico, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode statistique, Statistical method, Método estadístico, Série temporelle, Time series, Serie temporal, Théorie prévision, Forecasting theory, Coupure structurale, Structural break, Estimation fenêtre roulante, Rolling window estimation, MSFE, Propriété petit échantillon, Small sample property, Autoregression, C22, C53 Small sample properties of forecasts, Rolling window estimator, Structural breaks
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Faculty of Economics and Politics, University of Cambridge and USC, Sidgwick Avenue, Cambridge CB3 9DD, United Kingdom
Rady School of management and Department of Economics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0508, United States
ISSN:
0304-4076
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17222100
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.17222100
Database:
PASCAL Archive

Further Information

This paper develops a theoretical framework for the analysis of small-sample properties of forecasts from general autoregressive models under structural breaks. Finite-sample results for the mean squared forecast error of one-step ahead forecasts are derived, both conditionally and unconditionally, and numerical results for different types of break specifications are presented. It is established that forecast errors are unconditionally unbiased even in the presence of breaks in the autoregressive coefficients and/or error variances so long as the unconditional mean of the process remains unchanged. Insights from the theoretical analysis are demonstrated in Monte Carlo simulations and on a range of macroeconomic time series from G7 countries. The results are used to draw practical recommendations for the choice of estimation window when forecasting from autoregressive models subject to breaks.