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An R-Based Landscape Validation of a Competing Risk Model

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Published on: September 16, 2022

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

Ufuk Beyaztas¹, Han Lin Shang²

¹Department of Economics and Finance, Piri Reis University University, Istanbul, Turkey.

Journal of Applied Statistics

|June 16, 2022

Summary

This study introduces a robust bootstrap algorithm to improve prediction intervals for autoregressive time series, especially when data contains outliers. The new method enhances forecasting accuracy by using weighted estimates and residuals.

Keywords:

Autoregression multivariate forecast prediction interval resampling methods vector autoregression weighted likelihood

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Area of Science:

Time Series Analysis
Econometrics
Statistical Modeling

Background:

Autoregressive time series models are widely used in econometrics.
Outlying data points in these models lead to high forecast errors.
Existing bootstrap prediction intervals are sensitive to outliers, reducing forecasting performance.

Purpose of the Study:

To propose a robust bootstrap algorithm for constructing prediction intervals and forecast regions in autoregressive time series.
To address the limitations of non-robust estimators in the presence of outlying data points.
To improve the accuracy of forecasting in real-world applications.

Main Methods:

Development of a robust bootstrap algorithm based on weighted likelihood estimates.
Utilizing weighted residuals for interval construction.
Examination of finite sample properties through Monte Carlo simulations.

Main Results:

The proposed robust bootstrap algorithm demonstrates improved prediction interval construction.
The method effectively handles outlying data points, reducing forecast errors.
Empirical data examples validate the algorithm's practical performance.

Conclusions:

The robust bootstrap procedure offers a more reliable framework for prediction intervals in autoregressive models with outliers.
This approach enhances forecasting performance in econometrics and other fields.
The method provides a valuable tool for robust statistical inference with time series data.