Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/223547
Authors: 
Chudik, Alexander
Pesaran, M. Hashem
Sharifvaghefi, Mahrad
Year of Publication: 
2020
Series/Report no.: 
CESifo Working Paper No. 8475
Abstract: 
This paper is concerned with problem of variable selection and forecasting in the presence of parameter instability. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows or exponential down-weighting. However, these studies start with a given model specification and do not consider the problem of variable selection. It is clear that, in the absence of breaks, researchers should weigh the observations equally at both variable selection and forecasting stages. In this study, we investigate whether or not we should use weighted observations at the variable selection stage in the presence of structural breaks, particularly when the number of potential covariates is large. Amongst the extant variable selection approaches we focus on the recently developed One Covariate at a time Multiple Testing (OCMT) method that allows a natural distinction between the selection and forecasting stages, and provide theoretical justification for using the full (not down-weighted) sample in the selection stage of OCMT and down-weighting of observations only at the forecasting stage (if needed). The benefits of the proposed method are illustrated by empirical applications to forecasting output growths and stock market returns.
Subjects: 
time-varying parameters
structural breaks
high-dimensionality
multiple testing
variable selection
one covariate at a time multiple testing (OCMT)
forecasting
JEL: 
C22
C52
C53
C55
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.