Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Bootstrap prediction for returns and volatilities in GARCH models
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
A time series bootstrap procedure for interpolation intervals
Computational Statistics & Data Analysis
Approximate regenerative-block bootstrap for Markov chains
Computational Statistics & Data Analysis
Simulated minimum Hellinger distance estimation of stochastic volatility models
Computational Statistics & Data Analysis
Winter Simulation Conference
Polarization of forecast densities: A new approach to time series classification
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on the bootstrap is considered. Three methods are considered for countering the small-sample bias of least-squares estimation for processes which have roots close to the unit circle: a bootstrap bias-corrected OLS estimator; the use of the Roy-Fuller estimator in place of OLS; and the use of the Andrews-Chen estimator in place of OLS. All three methods of bias correction yield superior results to the bootstrap in the absence of bias correction. Of the three correction methods, the bootstrap prediction intervals based on the Roy-Fuller estimator are generally superior to the other two. The small-sample performance of bootstrap prediction intervals based on the Roy-Fuller estimator are investigated when the order of the AR model is unknown, and has to be determined using an information criterion.