High-Dimensional Forecasting in the Presence of Unit Roots and Cointegration

Stephan Smeekes, Etienne Wijler

Research output: Working paper / PreprintPreprint


We investigate how the possible presence of unit roots and cointegration affects forecasting with Big Data. As most macroeoconomic time series are very persistent and may contain unit roots, a proper handling of unit roots and cointegration is of paramount importance for macroeconomic forecasting. The high-dimensional nature of Big Data complicates the analysis of unit roots and cointegration in two ways. First, transformations to stationarity require performing many unit root tests, increasing room for errors in the classification. Second, modelling unit roots and cointegration directly is more difficult, as standard high-dimensional techniques such as factor models and penalized regression are not directly applicable to (co)integrated data and need to be adapted. We provide an overview of both issues and review methods proposed to address these issues. These methods are also illustrated with two empirical applications.
Original languageEnglish
PublisherCornell University - arXiv
Publication statusPublished - 24 Nov 2019

Publication series



  • high-dimensional time series
  • forecasting
  • unit roots
  • cointegration
  • factor models
  • penalized regression


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  • Unit Roots and Cointegration

    Smeekes, S. & Wijler, E., 2020, Macroeconomic Forecasting in the Era of Big Data: Theory and Practice. Fuleky, P. (ed.). 1 ed. Springer Nature Switzerland AG, Vol. 52. p. 541-584 44 p. (Advanced Studies in Theoretical and Applied Econometrics).

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

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