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Abstract

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

Fuente: https://mpra.ub.uni-muenchen.de/13086/







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