Finite-sample resampling-based combined hypothesis tests, with applications to serial correlation and predictability
Jean-Marie Dufour (),
Lynda Khalaf () and
CIRANO Working Papers from CIRANO
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 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6) Track citations by RSS feed
Downloads: (external link)
Working Paper: Finite-Sample Resampling-Based Combined Hypothesis Tests, with Applications to Serial Correlation and Predictability (2013)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:cir:cirwor:2013s-40
Access Statistics for this paper
More papers in CIRANO Working Papers from CIRANO Contact information at EDIRC.
Bibliographic data for series maintained by Webmaster ().