Variable Selection in High Dimensional Linear Regressions with Parameter Instability

Alexander Chudik, Mohammad Pesaran and Mahrad Sharifvaghefi

No 394, Globalization Institute Working Papers from Federal Reserve Bank of Dallas

Abstract: This paper considers the problem of variable selection allowing for parameter instability. It distinguishes between signal and pseudo-signal variables that are correlated with the target variable, and noise variables that are not, and investigates the asymptotic properties of the One Covariate at a Time Multiple Testing (OCMT) method proposed by Chudik et al. (2018) under parameter insatiability. It is established that OCMT continues to asymptotically select an approximating model that includes all the signals and none of the noise variables. Properties of post selection regressions are also investigated, and in-sample fit of the selected regression is shown to have the oracle property. The theoretical results support the use of unweighted observations at the selection stage of OCMT, whilst applying down-weighting of observations only at the forecasting stage. Monte Carlo and empirical applications show that OCMT without down-weighting at the selection stage yields smaller mean squared forecast errors compared to Lasso, Adaptive Lasso and boosting.

Keywords: Lasso; one covariate at a time multiple testing (OCMT); parameter instability; variable selection; forecasting (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 C55 (search for similar items in EconPapers)
Pages: 131
Date: 2020-08-19, Revised 2024-08-05
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
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Related works:
Working Paper: Variable Selection in High Dimensional Linear Regressions with Parameter Instability (2024)
Working Paper: Variable Selection in High Dimensional Linear Regressions with Parameter Instability (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:feddgw:88638

DOI: 10.24149/gwp394r3

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