Abstract
The bootstrap procedure has emerged as a general framework to construct prediction intervals for future observations in autoregressive time series models. Such models with outlying data points are standard in real data applications, especially in the field of econometrics. These outlying data points tend to produce high forecast errors, which reduce the forecasting performances of the existing bootstrap prediction intervals calculated based on non-robust estimators. In the univariate and multivariate autoregressive time series, we propose a robust bootstrap algorithm for constructing prediction intervals and forecast regions. The proposed procedure is based on the weighted likelihood estimates and weighted residuals. Its finite sample properties are examined via a series of Monte Carlo studies and two empirical data examples.
Original language | English |
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Pages (from-to) | 1179-1202 |
Number of pages | 24 |
Journal | Journal of Applied Statistics |
Volume | 49 |
Issue number | 5 |
Early online date | 1 Dec 2020 |
DOIs | |
Publication status | Published - 4 Apr 2022 |
Keywords
- Autoregression
- multivariate forecast
- prediction interval
- resampling methods
- vector autoregression
- weighted likelihood