Robust bootstrap prediction intervals for univariate and multivariate autoregressive time series models

Ufuk Beyaztas*, Han Lin Shang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Number of pages24
JournalJournal of Applied Statistics
DOIs
Publication statusE-pub ahead of print - 1 Dec 2020

Keywords

  • Autoregression
  • multivariate forecast
  • prediction interval
  • resampling methods
  • vector autoregression
  • weighted likelihood

Fingerprint Dive into the research topics of 'Robust bootstrap prediction intervals for univariate and multivariate autoregressive time series models'. Together they form a unique fingerprint.

Cite this