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A simple procedure for computing improved prediction intervals for autoregressive models

โœ Scribed by Paolo Vidoni


Book ID
111040110
Publisher
John Wiley and Sons
Year
2009
Tongue
English
Weight
532 KB
Volume
30
Category
Article
ISSN
0143-9782

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โœ Siu Hung Cheung; Ka Ho Wu; Wai Sum Chan ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 561 KB

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

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## Abstract Recent studies on bootstrap prediction intervals for autoregressive (AR) model provide simulation findings when the lag order is known. In practical applications, however, the AR lag order is unknown or can even be infinite. This paper is concerned with prediction intervals for AR model