Bootstrap prediction intervals for SETAR models
AbstractThis 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 27 (2011)
Issue (Month): 2 ()
Contact details of provider:
Web page: http://www.elsevier.com/locate/ijforecast
Bootstrap; Interval forecasting; SETAR models; Time series; Simulation;
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- 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.
- Menzie Chinn & Laurent Ferrara & Valérie Mignon, 2013. "Post-Recession US Employment through the Lens of a Non-Linear Okun's Law," Working Papers 2013-13, CEPII research center.
- Menzie Chinn & Laurent Ferrara & Valérie Mignon, 2013. "Post-recession US employment through the lens of a non-linear Okun’s law," EconomiX Working Papers 2013-12, University of Paris West - Nanterre la Défense, EconomiX.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If references are entirely missing, you can add them using this form.