Dette, HolgerWu, Weichi2020-01-172020-01-172020http://hdl.handle.net/2003/3853010.17877/DE290R-20449We develop an estimator for the high-dimensional covariance matrix of a locally stationary process with a smoothly varying trend and use this statistic to derive consistent predictors in non-stationary time series. In contrast to the currently available methods for this problem the predictor developed here does not rely on fitting an autoregressive model and does not require a vanishing trend. The finite sample properties of the new methodology are illustrated by means of a simulation study and a data example.enDiscussion Paper / SFB823;1/2020locally stationary time serieshigh dimensional auto-covariancematricespredictionlocal linear regression310330620working paper