Bootstrap prediction intervals for factor models
Silvia Goncalves (),
Benoit Perron and
Antoine Djogbenou ()
CIRANO Working Papers from CIRANO
We propose bootstrap prediction intervals for an observation h periods into the future and its conditional mean. We assume that these forecasts are made using a set of factors extracted from a large panel of variables. Because we treat these factors as latent, our forecasts depend both on estimated factors and estimated regression coefficients. Under regularity conditions, Bai and Ng (2006) proposed the construction of asymptotic intervals under Gaussianity of the innovations. The bootstrap allows us to relax this assumption and to construct valid prediction intervals under more general conditions. Moreover, even under Gaussianity, the bootstrap leads to more accurate intervals in cases where the cross-sectional dimension is relatively small as it reduces the bias of the OLS estimator as shown in a recent paper by Gonçalves and Perron (2014).
Keywords: factor model; bootstrap; forecast; conditional mean (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9) Track citations by RSS feed
Downloads: (external link)
Journal Article: Bootstrap Prediction Intervals for Factor Models (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:cir:cirwor:2016s-19
Access Statistics for this paper
More papers in CIRANO Working Papers from CIRANO Contact information at EDIRC.
Bibliographic data for series maintained by Webmaster ().