Arbeitspapier

Bootstrap tests of stochastic dominance with asymptotic similarity in the boundary

We propose a new method of testing stochastic dominance which improves on existing tests based on bootstrap or subsampling. Our test requires estimation of the contact sets between the marginal distributions. Our tests have asymptotic sizes that are exactly equal to the nominal level uniformly over the boundary points of the null hypothesis and are therefore valid over the whole null hypothesis. We also allow the prospects to be indexed by infinite as well as finite dimensional unknown parameters, so that the variables may be residuals from nonparametric and semiparametric models. Our simulation results show that our tests are indeed more powerful than the existing subsampling and recentered bootstrap.

Language
Englisch

Bibliographic citation
Series: cemmap working paper ; No. CWP08/08

Classification
Wirtschaft
Hypothesis Testing: General
Semiparametric and Nonparametric Methods: General
Model Evaluation, Validation, and Selection
Subject
Set estimation , Size of test , Unbiasedness , Similarity , Bootstrap , Subsampling
Bootstrap-Verfahren
Stochastischer Prozess
Nichtparametrisches Verfahren

Event
Geistige Schöpfung
(who)
Linton, Oliver
Song, Kyungchul
Whang, Yoon-Jae
Event
Veröffentlichung
(who)
Centre for Microdata Methods and Practice (cemmap)
(where)
London
(when)
2008

DOI
doi:10.1920/wp.cem.2008.0808
Handle
Last update
10.03.2025, 11:45 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Linton, Oliver
  • Song, Kyungchul
  • Whang, Yoon-Jae
  • Centre for Microdata Methods and Practice (cemmap)

Time of origin

  • 2008

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