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Testing Distributional Assumptions: A GMM Approach

Bontemps, Christian and Meddahi, Nour (2012) Testing Distributional Assumptions: A GMM Approach. Journal of Applied Econometrics, vol. 27 (n° 6). pp. 978-1012.

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Identification Number : 10.1002/jae.1250


We consider testing distributional assumptions by using moment conditions. A general class of moment conditions satisfied under the null hypothesis is derived and connected to existing moment-based tests. The approach is simple and easy to implement, yet reasonably powerful. In addition, we provide moment tests that are robust against parameter estimation error uncertainty in the general case which includes the case of serial correlation. In particular, we consider the location-scale model for which we derive robust moment tests, regardless of the forms of the conditional mean and variance. We study in detail the Student and inverse Gaussian distributions. Simulation experiments are conducted to assess the finite sample properties of the tests. We provide two empirical examples on foreign exchange rates by testing the Student distributional assumption of T-GARCH daily returns and on daily realized variance by testing the inverse Gaussian distributional assumption.

Item Type: Article
Language: English
Date: September 2012
Refereed: Yes
JEL Classification: C12 - Hypothesis Testing
C15 - Simulation Methods
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 09 Jul 2014 17:20
Last Modified: 02 Apr 2021 15:47
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