Size-Dependent Probability Bounds for t-Tests

Alessandro Palandri

Journal of Statistical and Econometric Methods, 2020, vol. 9, issue 3, 1

Abstract: The paper extends Chebyshev’s inequality to incorporate moments’ convergence in t-tests of model parameters. Size-dependent probability bounds are derived from one conditional higher-order moment of the distribution of the test statistic. Monte Carlo simulations attest that, in the cases of heteroskedastic and autocorrelated observations, the proposed bounds over-reject less than the asymptotic approximation and bootstrap methods. Therefore, when asymptotic critical values are suspected to lead to the over-rejection of the null hypothesis, the proposed inequalities may be used in conjunction to bootstrap methods to reduce the number of instances in which multiple re-samplings and associated estimations have to be performed.JEL classiï¬ cation numbers: C01, C12, C15. Keywords: Chebyshev’s Inequality, Asymptotic Approximation, Over Rejection, Bootstrap, Wild Bootstrap.

Date: 2020
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