Kernel smoothed prediction intervals for ARMA models

Klaus Abberger

No 02-02, CoFE Discussion Paper from Center of Finance and Econometrics, University of Konstanz

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 the sample forecast errors. In this paper we apply nonparametric quantile regression to the empirical forecast errors using lead time as regressor. With this method there is no need for a distribution assumption. But for the data pattern in this case a double kernel method which allows smoothing in two directions is required. An estimation algorithm is presented and applied to some simulation examples.

Keywords: Forecasting; Prediction intervals; Non normal distributions; Nonparametric estimation; Quantile regression (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets, nep-for, nep-ict and nep-rmg
Date: 2002-01
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