High-dimensional predictive regression in the presence of cointegration

Bonsoo Koo, Heather M. Anderson, Myung Hwan Seo, Wenying Yao

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)456-477
Number of pages22
JournalJournal of Econometrics
Issue number2
Publication statusPublished - Dec 2020


  • Cointegration
  • High-dimensional predictive regression
  • Return predictability

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