Skip to Main content Skip to Navigation
Journal articles

Testing distributional assumptions: A GMM aproach

Abstract : 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.
Document type :
Journal articles
Complete list of metadata

Cited literature [64 references]  Display  Hide  Download
Contributor : Christian BONTEMPS Connect in order to contact the contributor
Submitted on : Friday, June 19, 2020 - 2:26:18 PM
Last modification on : Tuesday, October 19, 2021 - 11:02:55 AM


Files produced by the author(s)




Christian Bontemps, Nour Meddahi. Testing distributional assumptions: A GMM aproach. Journal of Applied Econometrics, Wiley, 2012, 27 (6), pp.978-1012. ⟨10.1002/jae.1250⟩. ⟨hal-02875123⟩



Record views


Files downloads