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Bootstrapping factor-augmented regression models

  • Sílvia Gonçalves
  • Benoit Perron

The main contribution of this paper is to propose and theoretically justify bootstrap methods for regressions where some of the regressors are factors estimated from a large panel of data. We derive our results under the assumption that √T/N→c, where 0≤c0, a two-step residual-based bootstrap is required to capture the factors estimation uncertainty, which shows up as an asymptotic bias term (as we show here and as was recently discussed by Ludvigson and Ng (2009b)). Because the bias depends on the cross sectional dependence of the idiosyncratic error term, bootstrap validity depends crucially on the ability of the bootstrap panel factor model to capture this cross sectional dependence. Cet article propose et justifie théoriquement des méthodes de bootstrap pour des régressions où certains régresseurs sont des facteurs estimés à partir de panel de données de grandes dimensions. Nous obtenons nos résultats sous la condition que √T/N→c, où 0≤c 0, une procédure de bootstrap à deux étapes est nécessaire pour capter l'incertitude reliée à l'estimation des facteurs qui apparaît comme un biais asymptotique (tel que discuté récemment par Ludvigson et Ng (2009b). Parce que ce biais dépend de la dépendance transversale des erreurs idiosyncrasiques, la validité du bootstrap dépend de sa capacité à reproduire cette dépendance.

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File URL: http://www.cirano.qc.ca/files/publications/2012s-12.pdf
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Paper provided by CIRANO in its series CIRANO Working Papers with number 2012s-12.

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Length: 59 pages
Date of creation: 01 May 2012
Date of revision:
Handle: RePEc:cir:cirwor:2012s-12
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