Relative error accurate statistic based on nonparametric likelihood

Lorenzo Camponovo and Taisuke Otsu

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

Abstract: This paper develops a new test statistic for parameters defined by moment conditions that exhibits desirable relative error properties for the approximation of tail area probabilities. Our statistic, called the tilted exponential tilting (TET) statistic, is constructed by estimating certain cumulant generating function under exponential tilting weights. We show that the asymptotic p-value of the TET statistic can provide an accurate approximation to the p-value of an infeasible saddlepoint statistic, which is asymptotically chi-squared distributed with a relative error of order n−1 both in normal and large deviation regions. Numerical results illustrate the accuracy of the proposed TET statistic. Our results cover both just- and over-identified moment condition models.

Keywords: Nonparametric likelihood; Saddlepoint; Moment condition model (search for similar items in EconPapers)
JEL-codes: C12 C14 (search for similar items in EconPapers)
Date: 2017-11
New Economics Papers: this item is included in nep-ecm and nep-ore
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https://sticerd.lse.ac.uk/dps/em/em593.pdf (application/pdf)

Related works:
Working Paper: Relative Error Accurate Statistic Based on Nonparametric Likelihood (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:593

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