Selection of estimation window in the presence of breaks

In situations where a regression model is subject to one or more breaks it is shown that it can be optimal to use pre-break data to estimate the parameters of the model used to compute out-of-sample forecasts. The issue of how best to exploit the trade-off that might exist between bias and forecast...

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Veröffentlicht in:	Journal of econometrics Jg. 137; H. 1; S. 134 - 161
Hauptverfasser:	Pesaran, M. Hashem, Timmermann, Allan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.03.2007 Elsevier Elsevier Sequoia S.A
Schriftenreihe:	Journal of Econometrics
Schlagworte:	Applications Bias Choice of observation window Economic models Estimation Exact sciences and technology Forecast combination Forecasting Forecasting techniques Forecasting with breaks Forecasts Inference from stochastic processes; time series analysis Insurance, economics, finance Mathematics Monte Carlo simulation Parameter estimation Parameter instability Probability and statistics Regression analysis Sciences and techniques of general use Statistics Studies Weighting C53 C22 Forecasting with breaks Choice of observation window Parameter instability Forecast combination Monte Carlo method Data analysis Error estimation Bias Multivariate regression Cross validation Forecast model Variance analysis Statistical method Trade Selection problem Regression model Econometrics Biased estimation
ISSN:	0304-4076, 1872-6895
Online-Zugang:	Volltext
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