Multiple forecasts for autoregressive-integrated moving-average (ARIMA) models are useful in many areas such as economics and business forecasting. In recent years, approximation methods to construct simultaneous prediction intervals for multiple forecasts arc developed. These methods were based on
Forecasting with prediction intervals for periodic autoregressive moving average models
β Scribed by Paul L. Anderson; Mark M. Meerschaert; Kai Zhang
- Book ID
- 119878450
- Publisher
- John Wiley and Sons
- Year
- 2012
- Tongue
- English
- Weight
- 384 KB
- Volume
- 34
- Category
- Article
- ISSN
- 0143-9782
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Using the 'standard' approach to forecasting in the vector autoregressive moving average model, we establish basic general results on exact finite sample forecasts and their mean squared error matrices. Comparison between the exact and conditional methods of initiating the finite sample forecast cal
## Abstract This paper proposes the use of the biasβcorrected bootstrap for interval forecasting of an autoregressive time series with an arbitrary number of deterministic components. We use the biasβcorrected bootstrap based on two alternative biasβcorrection methods: the bootstrap and an analytic