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Quantile regression models with factor‐augmented predictors and information criterion

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  • Tomohiro Ando
  • Ruey S. Tsay

Abstract

For situations with a large number of series, N, each with T observations and each containing a certain amount of information for prediction of the variable of interest, we propose a new statistical modelling methodology that first estimates the common factors from a panel of data using principal component analysis and then employs the estimated factors in a standard quantile regression. A crucial step in the model‐building process is the selection of a good model among many possible candidates. Taking into account the effect of estimated regressors, we develop an information‐theoretic criterion. We also investigate the criterion when there is no estimated regressors. Results of Monte Carlo simulations demonstrate that the proposed criterion performs well in a wide range of situations.
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Suggested Citation

  • Tomohiro Ando & Ruey S. Tsay, 2011. "Quantile regression models with factor‐augmented predictors and information criterion," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 1-24, February.
  • Handle: RePEc:ect:emjrnl:v:14:y:2011:i:1:p:1-24
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    File URL: http://hdl.handle.net/10.1111/j.1368-423X.2010.00320.x
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    Cited by:

    1. Aslanidis, Nektarios & Christiansen, Charlotte, 2014. "Quantiles of the realized stock–bond correlation and links to the macroeconomy," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 321-331.
    2. Shih-Kang Chao & Wolfgang K. Härdle & Ming Yuan, 2016. "Factorisable Multi-Task Quantile Regression," SFB 649 Discussion Papers SFB649DP2016-057, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Bai, Jushan & Ando, Tomohiro, 2013. "Multifactor asset pricing with a large number of observable risk factors and unobservable common and group-specific factors," MPRA Paper 52785, University Library of Munich, Germany, revised Dec 2013.
    4. Harding, Matthew & Lamarche, Carlos, 2014. "Estimating and testing a quantile regression model with interactive effects," Journal of Econometrics, Elsevier, vol. 178(P1), pages 101-113.
    5. Chen Jau-er, 2015. "Factor instrumental variable quantile regression," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 71-92, February.
    6. repec:eee:intfin:v:50:y:2017:i:c:p:52-68 is not listed on IDEAS
    7. Siklos, Pierre L., 2012. "No coupling, no decoupling, only mutual inter-dependence : Business cycles in emerging vs. mature economies," BOFIT Discussion Papers 17/2012, Bank of Finland, Institute for Economies in Transition.
    8. Giglio, Stefano & Kelly, Bryan & Pruitt, Seth, 2016. "Systemic risk and the macroeconomy: An empirical evaluation," Journal of Financial Economics, Elsevier, vol. 119(3), pages 457-471.

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