  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.
