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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Journal of econometrics Ročník 137; číslo 1; s. 134 - 161
Hlavní autoři:	Pesaran, M. Hashem, Timmermann, Allan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 01.03.2007 Elsevier Elsevier Sequoia S.A
Edice:	Journal of Econometrics
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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 error variance is explored and illustrated for the multivariate regression model under the assumption of strictly exogenous regressors. In practice when this assumption cannot be maintained and both the time and size of the breaks are unknown, the optimal choice of the observation window will be subject to further uncertainties that make exploiting the bias–variance trade-off difficult. To that end we propose a new set of cross-validation methods for selection of a single estimation window and weighting or pooling methods for combination of forecasts based on estimation windows of different lengths. Monte Carlo simulations are used to show when these procedures work well compared with methods that ignore the presence of breaks.
Bibliografie:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	0304-4076 1872-6895
DOI:	10.1016/j.jeconom.2006.03.010