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This 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.

Item Type: MPRA Paper -

Original Title: Bootstrap prediction intervals for threshold autoregressive models-

Language: English-

Keywords: Bootstrap; Interval Forecasting; Threshold Autoregressive Models; Time Series; Simulation-

Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation MethodsC - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion ProcessesC - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General-

Autor: Jing, Li


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