Robustness of sign tests in autoregression

Boldin, M. V.

doi:10.3103/s0027132212030059

Cited by 3 publications

(2 citation statements)

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“…Despite their simplicity, the sign tests do extremely well (confirming univariate robustness results by Boldin (2012Boldin ( , 2013) but remain less powerful than the bias-corrected vdW tests.…”

Section: Numerical Results: Size Power and Bias Correctionmentioning

confidence: 81%

Rank-Based Testing for Semiparametric VAR Models: a measure transportation approach

Hallin¹,

Vecchia²,

Liu³

2020

Preprint

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We develop a class of tests for semiparametric vector autoregressive (VAR) models with unspecified innovation densities, based on the recent measure-transportation-based concepts of multivariate center-outward ranks and signs. We show that these concepts, combined with Le Cam's asymptotic theory of statistical experiments, yield novel testing procedures, which (a) are valid under a broad class of innovation densities (possibly non-elliptical, skewed, and/or with infinite moments), (b) are optimal (locally asymptotically maximin or most stringent) at selected ones, and (c) are robust against additive outliers. In order to do so, we establish a Hájek asymptotic representation result, of independent interest, for a general class of center-outward rank-based serial statistics. As an illustration, we consider the problems of testing the absence of serial correlation in multiple-output and possibly non-linear regression (an extension of the classical Durbin-Watson problem) and the sequential identification of the order p of a vector autoregressive (VAR(p)) model. A Monte Carlo comparative study of our tests and their routinely-applied Gaussian competitors demonstrates the benefits (in terms of size, power, and robustness) of our methodology; these benefits are particularly significant in the presence of asymmetric and leptokurtic innovation densities. A real data application concludes the paper.

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Section: Numerical Results: Size Power and Bias Correctionmentioning

confidence: 81%

Rank-Based Testing for Semiparametric VAR Models: a measure transportation approach

Hallin¹,

Vecchia²,

Liu³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Other reviews: H-Net https://networks.h-net.org/node/1883/pages/144668/new-book-struggle-save-soviet-economy-mikhailgorbachev-and-collapse [2] Source URL: https://reviews.history.ac.uk/review/2066 Links [1] https://reviews.history.ac.uk/item/252204 [2] https://networks.h-net.org/node/1883/pages/144668/new-book-struggle-save-soviet-economy-mikhailgorbachev-and-collapse…”

mentioning

confidence: 99%

Review of 'The Struggle to Save the Soviet Economy: Mikhail Gorbachev and the Collapse of the USSR'

Scarborough¹

2017

RiH

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Rank-based testing for semiparametric VAR models: A measure transportation approach

Hallin¹,

Vecchia²,

Liu³

2023

Bernoulli

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Robustness of sign tests in autoregression

Cited by 3 publications

References 10 publications

Rank-Based Testing for Semiparametric VAR Models: a measure transportation approach

Rank-Based Testing for Semiparametric VAR Models: a measure transportation approach

Review of 'The Struggle to Save the Soviet Economy: Mikhail Gorbachev and the Collapse of the USSR'

Rank-based testing for semiparametric VAR models: A measure transportation approach

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