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Nuisance parameters, composite likelihoods and a panel of GARCH models

  • Cavit Pakel

    ()

    (Department of Economics and Oxford-Man Institute, University of Oxford, Oxford)

  • Neil Shephard

    ()

    (Oxford-Man Institute and Department of Economics, University of Oxford, Oxford)

  • Kevin Sheppard

    ()

    (Oxford-Man Institute and Department of Economics, University of Oxford, Oxford)

We investigate the properties of the composite likelihood (CL) method for (T ×N_T ) GARCH panels. The defining feature of a GARCH panel with time series length T is that, while nuisance parameters are allowed to vary across N_T series, other parameters of interest are assumed to be common. CL pools information across the panel instead of using information available in a single series only. Simulations and empirical analysis illustrate that in reasonably large T CL performs well. However, due to the estimation error introduced through nuisance parameter estimation, CL is subject to the “incidental parameter” problem for small T.

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File URL: http://www.nuffield.ox.ac.uk/economics/papers/2009/w12/cavit300909.pdf
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Paper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2009-W12.

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Length: 22 pages
Date of creation: 02 Oct 2009
Date of revision:
Handle: RePEc:nuf:econwp:0912
Contact details of provider: Web page: http://www.nuff.ox.ac.uk/economics/

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  1. Andrew Patton, 2006. "Volatility Forecast Comparison using Imperfect Volatility Proxies," Research Paper Series 175, Quantitative Finance Research Centre, University of Technology, Sydney.
  2. Lancaster, Tony, 2000. "The incidental parameter problem since 1948," Journal of Econometrics, Elsevier, vol. 95(2), pages 391-413, April.
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  8. Giacomini, Raffaella & White, Halbert, 2003. "Tests of Conditional Predictive Ability," University of California at San Diego, Economics Working Paper Series qt5jk0j5jh, Department of Economics, UC San Diego.
  9. N. Sartori, 2003. "Modified profile likelihoods in models with stratum nuisance parameters," Biometrika, Biometrika Trust, vol. 90(3), pages 533-549, September.
  10. Robert Engle & Neil Shephard & Kevin Shepphard, 2008. "Fitting vast dimensional time-varying covariance models," OFRC Working Papers Series 2008fe30, Oxford Financial Research Centre.
  11. Donald W.K. Andrews, 1988. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Cowles Foundation Discussion Papers 877R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1989.
  12. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
  13. Cristiano Varin, 2008. "On composite marginal likelihoods," AStA Advances in Statistical Analysis, Springer, vol. 92(1), pages 1-28, February.
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  16. Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series qt5s2218dp, Department of Economics, UC San Diego.
  17. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
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