Prediction in locally stationary time series
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Date
2020
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Abstract
We 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.
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Keywords
locally stationary time series, high dimensional auto-covariance, matrices, prediction, local linear regression