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Bootstrap prediction intervals for factor models

Silvia Goncalves (), Benoit Perron and Antoine Djogbenou ()

CIRANO Working Papers from CIRANO

Abstract: 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
Date: 2016-04-11
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Journal Article: Bootstrap Prediction Intervals for Factor Models (2017) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:cir:cirwor:2016s-19

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