High-dimensional predictive regression in the presence of cointegration

Bonsoo Koo, Heather Anderson, Myung Hwan Seo and Wenying Yao

Journal of Econometrics, 2020, vol. 219, issue 2, 456-477

Abstract: We propose a Least Absolute Shrinkage and Selection Operator (LASSO) estimator of a predictive regression in which stock returns are conditioned on a large set of lagged covariates, some of which are highly persistent and potentially cointegrated. We establish the asymptotic properties of the proposed LASSO estimator and validate our theoretical findings using simulation studies. The application of this proposed LASSO approach to forecasting stock returns suggests that a cointegrating relationship among the persistent predictors leads to a significant improvement in the prediction of stock returns over various competing forecasting methods with respect to mean squared error.

Keywords: Cointegration; High-dimensional predictive regression; LASSO; Return predictability (search for similar items in EconPapers)
JEL-codes: C13 C22 G12 G17 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:219:y:2020:i:2:p:456-477

DOI: 10.1016/j.jeconom.2020.03.011

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