Kernel smoothed prediction intervals for ARMA models
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.(This abstract was borrowed from another version of this item.)
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Bibliographic Info
Article provided by Springer in its journal Statistical Papers.
Volume (Year): 47 (2006)
Issue (Month): 1 (January)
Pages: 1-15
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Web page: http://www.springer.com/statistics/business/journal/362
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Related research
Keywords: Forecasting; Prediction intervals; Non-normal distributions; Nonparametric estimation; Quantile regression;Other versions of this item:
- Klaus Abberger, 2002. "Kernel smoothed prediction intervals for ARMA models," CoFE Discussion Paper 02-02, Center of Finance and Econometrics, University of Konstanz.
References
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- 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.
- Chatfield, Chris, 1993. "Calculating Interval Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 143-44, April.
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