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Bootstrapping factor-augmented regression models

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  • Sílvia Gonçalves

    ()

  • Benoit Perron

    ()

Abstract

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≤c 0, 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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Bibliographic Info

Paper provided by CIRANO in its series CIRANO Working Papers with number 2012s-12.

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Date of creation: 01 May 2012
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Handle: RePEc:cir:cirwor:2012s-12

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Keywords: factor model; bootstrap; asymptotic bias; Modèle à facteurs; bootstrap; biais asymptotique;

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References

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  1. Barry Eichengreen & Ashoka Mody & Milan Nedeljkovic & Lucio Sarno, 2009. "How the Subprime Crisis Went Global: Evidence from Bank Credit Default Swap Spreads," NBER Working Papers 14904, National Bureau of Economic Research, Inc.
  2. Nikolay Gospodinov & Serena Ng, 2013. "Commodity Prices, Convenience Yields, and Inflation," The Review of Economics and Statistics, MIT Press, vol. 95(1), pages 206-219, March.
  3. Ludvigson, Sydney C. & Ng, Serena, 2007. "The empirical risk-return relation: A factor analysis approach," Journal of Financial Economics, Elsevier, vol. 83(1), pages 171-222, January.
  4. Sydney C. Ludvigson & Serena Ng, 2009. "Macro Factors in Bond Risk Premia," Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5027-5067, December.
  5. Connor, Gregory & Korajczyk, Robert A., 1986. "Performance measurement with the arbitrage pricing theory : A new framework for analysis," Journal of Financial Economics, Elsevier, vol. 15(3), pages 373-394, March.
  6. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
  7. Ben S. Bernanke & Jean Boivin, 2001. "Monetary Policy in a Data-Rich Environment," NBER Working Papers 8379, National Bureau of Economic Research, Inc.
  8. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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