Arbeitspapier
Variable Selection in High Dimensional Linear Regressions with Parameter Instability
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: CESifo Working Paper ; No. 10223
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Model Evaluation, Validation, and Selection
Forecasting Models; Simulation Methods
Large Data Sets: Modeling and Analysis
- Thema
-
parameter instability
high-dimensionality
variable selection
One Covariate at a time Multiple Testing (OCMT)
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Chudik, Alexander
Pesaran, M. Hashem
Sharifvaghefi, Mahrad
- Ereignis
-
Veröffentlichung
- (wer)
-
Center for Economic Studies and ifo Institute (CESifo)
- (wo)
-
Munich
- (wann)
-
2023
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Chudik, Alexander
- Pesaran, M. Hashem
- Sharifvaghefi, Mahrad
- Center for Economic Studies and ifo Institute (CESifo)
Entstanden
- 2023
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