Kernel smoothed prediction intervals for ARMA models
AbstractThe 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.
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Bibliographic InfoPaper provided by Center of Finance and Econometrics, University of Konstanz in its series CoFE Discussion Paper with number 02-02.
Length: 18 pages
Date of creation: Jan 2002
Date of revision:
Other versions of this item:
- Klaus Abberger, 2006. "Kernel smoothed prediction intervals for ARMA models," Statistical Papers, Springer, vol. 47(1), pages 1-15, January.
- NEP-ALL-2006-08-26 (All new papers)
- NEP-ETS-2006-08-26 (Econometric Time Series)
- NEP-FOR-2006-08-26 (Forecasting)
- NEP-ICT-2006-08-26 (Information & Communication Technologies)
- NEP-RMG-2006-08-26 (Risk Management)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Chatfield, Chris, 1993. "Calculating Interval Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 143-44, April.
- Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-35, April.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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