Finite-Sample Sign-Based Inference in Linear and Nonlinear Regression Models with Applications in Finance

Taamouti, A.

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.

Citation

Taamouti, A. (2015). Finite-Sample Sign-Based Inference in Linear and Nonlinear Regression Models with Applications in Finance. L'actualité économique (En ligne), 91(1-2), 89-113

• Journal Article Type: Article
• Acceptance Date: Jun 10, 2015
• Publication Date: Jun 1, 2015
• Deposit Date: Aug 14, 2015
• Publicly Available Date: Aug 21, 2015
• Journal: L'Actualité économique : revue d'analyse économique.
• Print ISSN: 0001-771X
• Electronic ISSN: 1710-3991
• Publisher: HEC Montréal
• Peer Reviewed: Peer Reviewed
• Volume: 91
• Issue: 1-2
• Pages: 89-113
• Keywords: Nonparametric tests, Exact inference, Sign test, Point-optimal test, Nonlinear model, Heteroskedasticity, Distribution-free, Random walk, Median regression, Monte Carlo test, Serial dependence, Stock return predictability, CAPM, Market efficiency.
• Public URL: https://durham-repository.worktribe.com/output/1401437
• Publisher URL: http://expertise.hec.ca/actualiteeconomique/wp-content/uploads/2016/03/91_1_2_2015_taamouti_89_113.pdf