Finite-Sample Resampling-Based Combined Hypothesis Tests, with Applications to Serial Correlation and Predictability

24 Pages Posted: 6 Mar 2014

See all articles by Jean-Marie Dufour

Jean-Marie Dufour

McGill University

Lynda Khalaf

Carleton University

Marcel C. Voia

Carleton University

Date Written: October 18, 2013

Abstract

This paper suggests Monte Carlo multiple test procedures which are provably valid in finite samples. These include combination methods originally proposed for independent statistics and further improvements which formalize statistical practice. We also adapt the Monte Carlo test method to non-continuous combined statistics. The methods suggested are applied to test serial dependence and predictability. In particular, we introduce and analyze new procedures that account for endogenous lag selection. A simulation study illustrates the properties of the proposed methods. Results show that concrete and non-spurious power gains (over standard combination methods) can be achieved through the combined Monte Carlo test approach, and confirm arguments in favour of variance-ratio type criteria.

Keywords: Monte Carlo test, induced test, test combination, simultaneous inference, Variance ratio

Suggested Citation

Dufour, Jean-Marie and Khalaf, Lynda and Voia, Marcel C., Finite-Sample Resampling-Based Combined Hypothesis Tests, with Applications to Serial Correlation and Predictability (October 18, 2013). CIRANO - Scientific Publications 2013s-40, Available at SSRN: https://ssrn.com/abstract=2369446 or http://dx.doi.org/10.2139/ssrn.2369446

Jean-Marie Dufour (Contact Author)

McGill University ( email )

Department of Economics, McGill University
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Lynda Khalaf

Carleton University ( email )

1125 colonel By Drive
Ottawa, Ontario K1S 5B6
Canada

Marcel C. Voia

Carleton University ( email )

1125 colonel By Drive
Ottawa, Ontario K1S 5B6
Canada

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