Bootstrap prediction intervals for threshold autoregressive models
AbstractThis paper examines the performance of prediction intervals based on bootstrap for threshold autoregressive models. We consider four bootstrap methods to account for the variability of estimates, correct the small-sample bias of autoregressive coefficients and allow for heterogeneous errors. Simulation shows that (1) accounting for the sampling variability of estimated threshold values is necessary despite super-consistency, (2) bias-correction leads to better prediction intervals under certain circumstances, and (3) two-sample bootstrap can improve long term forecast when errors are regime-dependent.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 13086.
Date of creation: Jan 2009
Date of revision:
Bootstrap; Interval Forecasting; Threshold Autoregressive Models; Time Series; Simulation;
Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-02-07 (All new papers)
- NEP-ECM-2009-02-07 (Econometrics)
- NEP-ETS-2009-02-07 (Econometric Time Series)
- NEP-FOR-2009-02-07 (Forecasting)
- NEP-ORE-2009-02-07 (Operations Research)
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