Résumés
Abstract
We review several exact sign-based tests that have been recently proposed for testing orthogonality between random variables in the context of linear and nonlinear regression models. The sign tests are very useful when the data at the hands contain few observations, are robust against heteroskedasticity of unknown form, and can be used in the presence of non-Gaussian errors. These tests are also flexible since they do not require the existence of moments for the dependent variable and there is no need to specify the nature of the feedback between the dependent variable and the current and future values of the independent variable. Finally, we discuss several applications where the sign-based tests can be used to test for multi-horizon predictability of stock returns and for the market efficiency.
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Parties annexes
Acknowledgements
We thank the Editors Marie-Claude Beaulieu, Jean-Marie Dufour, and Lynda Khalaf and an anonymous referee for his/her very useful comments. Financial support from Durham University Business School and the Spanish Ministry of Education through grants SEJ 2007-63098 and #ECO2010-19357 are acknowledged.
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