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Nuisance parameters, composite likelihoods and a panel of GARCH models

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Author Info
Cavit Pakel ()
Neil Shephard ()
Kevin Sheppard ()
Abstract

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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Paper provided by Oxford Financial Research Centre in its series OFRC Working Papers Series with number 2009fe03.

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Length: 22
Date of creation: 2009
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Handle: RePEc:sbs:wpsefe:2009fe03

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Related research
Keywords: ARCH models; composite likelihood; nuisance parameters; panel data.;

Find related papers by JEL classification:
C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

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  2. Arellano, Manuel & Honore, Bo, 2001. "Panel data models: some recent developments," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 53, pages 3229-3296 Elsevier. [Downloadable!] (restricted)
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  3. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-58, May. [Downloadable!] (restricted)
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  4. N. Sartori, 2003. "Modified profile likelihoods in models with stratum nuisance parameters," Biometrika, Oxford University Press for Biometrika Trust, vol. 90(3), pages 533-549, September.
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  6. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121. [Downloadable!] (restricted)
  7. L. Bauwens & J. V. K. Rombouts, 2007. "Bayesian Clustering of Many Garch Models," Econometric Reviews, Taylor and Francis Journals, vol. 26(2-4), pages 365-386. [Downloadable!] (restricted)
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  8. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November. [Downloadable!] (restricted)
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  9. Cristiano Varin, 2008. "On composite marginal likelihoods," AStA Advances in Statistical Analysis, Springer, vol. 92(1), pages 1-28, February. [Downloadable!] (restricted)
  10. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-84, September. [Downloadable!] (restricted)
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  11. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
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  12. Lancaster, Tony, 2000. "The incidental parameter problem since 1948," Journal of Econometrics, Elsevier, vol. 95(2), pages 391-413, April. [Downloadable!] (restricted)
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