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Finite-sample sign-based inference in linear and nonlinear regression models with applications in finance.

Taamouti, A. (2015) 'Finite-sample sign-based inference in linear and nonlinear regression models with applications in finance.', L'actualité économique : revue d'analyse économique., 91 (1-2). pp. 89-113.


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.

Item Type:Article
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.
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Record Created:21 Aug 2015 09:50
Last Modified:16 Mar 2016 16:47

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