Kernel smoothed prediction intervals for ARMA models |
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Authors: | Klaus Abberger |
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Institution: | (1) ifo Institute for Economic Research, Poschingerstr. 5, 81679 Munich, Germany |
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Abstract: | The procedures of estimating prediction intervals for ARMA processes can be divided into model based methods and empirical
methods. Model based methods require knowledge of the model and the underlying innovation distribution. Empirical methods
are based on sample forecast errors. In this paper we apply nonparametric quantile regression to empirical forecast errors
using lead time as regressor. Using this method there is no need for a distributional assumption. But for the special data
pattern in this application a double kernel method which allows smoothing in two directions is required. An estimation algorithm
is presented and applied to some simulation examples. |
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Keywords: | Forecasting Prediction intervals Non-normal distributions Nonparametric estimation Quantile regression |
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