Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust bootstrap prediction intervals for univariate and multivariate
  autoregressive time series models   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.

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The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
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The categories of this paper are: quant-ph