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Bootstrap prediction intervals for SETAR models

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  • Li, Jing
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    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 27 (2011)
    Issue (Month): 2 ()
    Pages: 320-332

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    Handle: RePEc:eee:intfor:v:27:y:2011:i:2:p:320-332

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Bootstrap; Interval forecasting; SETAR models; Time series; Simulation;

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    Cited by:
    1. Menzie D. Chinn & Laurent Ferrara & Valérie Mignon, 2013. "Post-recession US Employment through the Lens of a Non-linear Okun’s law," NBER Working Papers 19047, National Bureau of Economic Research, Inc.
    2. Frédérique Bec & Othman Bouabdallah & Laurent Ferrara, 2011. "The European Way Out of Recessions," THEMA Working Papers 2011-23, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.

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