Testing Distributional Assumptions: A GMM Approach
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
(This abstract was borrowed from another version of this item.)
|Date of creation:||Oct 2007|
|Date of revision:|
|Publication status:||Published in Journal of Applied Econometrics, vol. 27, n°6, septembre 2012, p. 978-1012.|
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- DUFOUR, Jean-Marie & FARHAT, Abdeljelil & GARDIOL, Lucien, 1998.
"Simulation-Based Finite-Sample Normality Tests in Linear Regressions,"
Cahiers de recherche
9811, Universite de Montreal, Departement de sciences economiques.
- Jean-Marie Dufour & Abdeljelil Farhat & Lucien Gardiol & Lynda Khalaf, 1998. "Simulation-based finite sample normality tests in linear regressions," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages C154-C173.
- Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
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