Bootstrap prediction intervals for SETAR models

Jing Li

International Journal of Forecasting, 2011, vol. 27, issue 2, 320-332

Abstract: This paper considers four methods for obtaining bootstrap prediction intervals (BPIs) for the self-exciting threshold autoregressive (SETAR) model. Method 1 ignores the sampling variability of the threshold parameter estimator. Method 2 corrects the finite sample biases of the autoregressive coefficient estimators before constructing BPIs. Method 3 takes into account the sampling variability of both the autoregressive coefficient estimators and the threshold parameter estimator. Method 4 resamples the residuals in each regime separately. A Monte Carlo experiment shows that (1) accounting for the sampling variability of the threshold parameter estimator is necessary, despite its super-consistency; (2) correcting the small-sample biases of the autoregressive parameter estimators improves the small-sample properties of bootstrap prediction intervals under certain circumstances; and (3) the two-sample bootstrap can improve the long-term forecasts when the error terms are regime-dependent.

Keywords: Bootstrap; Interval forecasting; SETAR models; Time series; Simulation (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:27:y:2011:i:2:p:320-332

DOI: 10.1016/j.ijforecast.2010.01.013

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