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Testing Distributional Assumptions: A GMM Approach

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  • Bontemps, Christian
  • Meddahi, Nour

Abstract

In this paper, we consider testing marginal distributional assumptions. Special cases that we consider are the Pearson's family like the Gaussian, Student, Gamma, Beta and uniform distributions. The test statistics we consider are based on the first moment conditions derived by Hansen and Scheinkman (1995) when one considers a continuous time model. These moment conditions are valid even if the observations are not a sample of a continuous time model. We treat in detail the parameter uncertainty problem when the considered process is not observed but depends on estimators of unknown parameters. We also consider the time series case and adopt a HAC approach for this purpose. This is a generalization of Bontemps and Meddahi (2002) who considered this approach for the Normal case

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Bibliographic Info

Paper provided by Institut d'Économie Industrielle (IDEI), Toulouse in its series IDEI Working Papers with number 486.

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Date of creation: Oct 2007
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Publication status: Published in Journal of Applied Econometrics, vol.�27, n°6, septembre 2012, p.�978-1012.
Handle: RePEc:ide:wpaper:5705

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Cited by:
  1. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive density and conditional confidence interval accuracy tests," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 187-228.
  2. Barbara Rossi & Tatevik Sekhposyan, 2014. "Alternative Tests for Correct Specification of Conditional Predictive Densities," Working Papers 758, Barcelona Graduate School of Economics.
  3. Xiaohong Chen & Lars Peter Hansen & Jose A. Scheinkman, 2009. "Principal components and the long run," CeMMAP working papers CWP07/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. Torben G. Andersen & Tim Bollerslev & Per Frederiksen & Morten Ørregaard Nielsen, 2008. "Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns," Working Papers 1173, Queen's University, Department of Economics.
  5. Christophe Hurlin & Gilbert Colletaz & Sessi Tokpavi & Bertrand Candelon, 2008. "Backtesting Value-at-Risk: A GMM Duration-Based Test," Working Papers halshs-00329495, HAL.
  6. Enrique Sentana & Gabriele Fiorentini, 2007. "On The Efficiency And Consistency Of Likelihood Estimation In Multivariate Conditionally Heteroskedastic Dynamic Regression Models," Working Papers wp2007_0713, CEMFI.
  7. Denisa Georgiana Banulescu & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2013. "High-Frequency Risk Measures," Working Papers halshs-00859456, HAL.
  8. Amengual, Dante & Fiorentini, Gabriele & Sentana, Enrique, 2013. "Sequential estimation of shape parameters in multivariate dynamic models," Journal of Econometrics, Elsevier, vol. 177(2), pages 233-249.
  9. Bontemps, Christian, 2013. "Moment-Based Tests for Discrete Distributions," IDEI Working Papers 772, Institut d'Économie Industrielle (IDEI), Toulouse.
  10. Andres, Philipp, 2014. "Maximum likelihood estimates for positive valued dynamic score models; The DySco package," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 34-42.

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