Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/271867 
Year of Publication: 
2023
Series/Report no.: 
CESifo Working Paper No. 10223
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Abstract: 
This paper is concerned with the problem of variable selection when the marginal effects of signals on the target variable as well as the correlation of the covariates in the active set are allowed to vary over time, without committing to any particular model of parameter instabilities. It poses the issue of whether weighted or unweighted observations should be used at the variable selection stage in the presence of parameter instability, particularly when the number of potential covariates is large. Amongst the extant variable selection approaches, we focus on the One Covariate at a time Multiple Testing (OCMT) method. This procedure allows a natural distinction between the selection and forecasting stages. We establish three main theorems on selection, estimation post selection, and in-sample fit. These theorems provide justification for using unweighted observations at the selection stage of OCMT and down-weighting of observations only at the forecasting stage. The benefits of the proposed method as compared to Lasso, Adaptive Lasso and Boosting are illustrated by Monte Carlo studies and empirical applications to forecasting monthly stock market returns and quarterly output growths.
Subjects: 
parameter instability
high-dimensionality
variable selection
One Covariate at a time Multiple Testing (OCMT)
JEL: 
C22
C52
C53
C55
Document Type: 
Working Paper
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