Model Selection when there are Multiple Breaks.

We consider selecting an econometric model when there is uncertainty over both the choice of variables and the occurrence and timing of multiple location shifts. The theory of general-to-simple (Gets) selection is outlined and its efficacy demonstrated in a new set of simulation experiments first f...

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Autori principali: Castle, J, Doornik, J, Hendry, D
Natura: Working paper
Lingua:English
Pubblicazione: Department of Economics (University of Oxford) 2008
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author Castle, J
Doornik, J
Hendry, D
author_facet Castle, J
Doornik, J
Hendry, D
author_sort Castle, J
collection OXFORD
description We consider selecting an econometric model when there is uncertainty over both the choice of variables and the occurrence and timing of multiple location shifts. The theory of general-to-simple (Gets) selection is outlined and its efficacy demonstrated in a new set of simulation experiments first for a constant model in orthogonal variables, where only one decision is required to select irrespective of the number of regressors (less than the sample size). That generalizes to including an impulse indicator for every observation in the set of candidate regressors (impulse saturation), as analyzed by Hendry, Johansen and Santos (2008) and Johansen and Nielsen (2009). Monte Carlo experiments show its capability of detecting up to 20 shifts in 100 observations.
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spelling oxford-uuid:42a3da85-d53c-4d21-a513-4a6f6c01c8322022-03-26T14:50:42ZModel Selection when there are Multiple Breaks.Working paperhttp://purl.org/coar/resource_type/c_8042uuid:42a3da85-d53c-4d21-a513-4a6f6c01c832EnglishDepartment of Economics - ePrintsDepartment of Economics (University of Oxford)2008Castle, JDoornik, JHendry, DWe consider selecting an econometric model when there is uncertainty over both the choice of variables and the occurrence and timing of multiple location shifts. The theory of general-to-simple (Gets) selection is outlined and its efficacy demonstrated in a new set of simulation experiments first for a constant model in orthogonal variables, where only one decision is required to select irrespective of the number of regressors (less than the sample size). That generalizes to including an impulse indicator for every observation in the set of candidate regressors (impulse saturation), as analyzed by Hendry, Johansen and Santos (2008) and Johansen and Nielsen (2009). Monte Carlo experiments show its capability of detecting up to 20 shifts in 100 observations.
spellingShingle Castle, J
Doornik, J
Hendry, D
Model Selection when there are Multiple Breaks.
title Model Selection when there are Multiple Breaks.
title_full Model Selection when there are Multiple Breaks.
title_fullStr Model Selection when there are Multiple Breaks.
title_full_unstemmed Model Selection when there are Multiple Breaks.
title_short Model Selection when there are Multiple Breaks.
title_sort model selection when there are multiple breaks
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AT doornikj modelselectionwhentherearemultiplebreaks
AT hendryd modelselectionwhentherearemultiplebreaks