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Two-Point Dependent Wild Bootstrap for Weakly Dependent Estimating Equations — arXiv:2609.32369

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.32369
License (as archived): CC0-1.0
Archived: 2026-10-07T23:03:26.191Z
SHA-256 of the archived text: 94a8b5cc709e780910373a9cdf73d2ca469dd0e6b4a75f5c3cfe1a8244271858
Stable URL of this page: https://metraipolis.com/documento/dc_cd1fee0975dc

How to cite

Two-Point Dependent Wild Bootstrap for Weakly Dependent Estimating Equations — arXiv:2609.32369 — arXiv (en), https://arxiv.org/abs/2609.32369 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cd1fee0975dc (Metraipolis archive dc_cd1fee0975dc, sha256 94a8b5cc709e780910373a9cdf73d2ca469dd0e6b4a75f5c3cfe1a8244271858)

BibTeX

@misc{dccd1fee0975dc,
  title = {Two-Point Dependent Wild Bootstrap for Weakly Dependent Estimating Equations — arXiv:2609.32369},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.32369},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cd1fee0975dc; sha256 94a8b5cc709e780910373a9cdf73d2ca469dd0e6b4a75f5c3cfe1a8244271858},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.32369. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This paper develops a general two-point dependent wild bootstrap (DWB) for weakly dependent estimating equations. Its key feature is that the two-point marginal distribution and the latent serial dependence specification can be chosen separately. The construction combines a normalized two-point distribution with a stationary latent Gaussian process via a Gaussian copula transformation, includes dependent Rademacher and Mammen multipliers, and nests the classical iid two-point wild bootstrap as the serially independent case. The induced multiplier autocovariances determine the lag weights in a corresponding heteroskedasticity- and autocorrelation-consistent (HAC) covariance estimator, which coincides exactly with the conditional covariance of the bootstrap estimating-equation sum. We establish first-order bootstrap validity for asymptotically linear estimators by showing that the matched-HAC estimator consistently estimates the long-run covariance and that the bootstrap estimating-equation sum converges conditionally to the same Gaussian limit as its original-sample counterpart, yielding valid HAC-studentized $z$-tests and the corresponding Wald and Lagrange multiplier tests. Monte Carlo experiments in nonlinear generalized method of moments (GMM) and linear regression show that Rademacher DWB generally provides more accurate finite-sample size control for $z$-tests than the Mammen and Gaussian DWB. A GMM application to a nonlinear short-rate mean-reversion model illustrates the practical relevance of the proposed two-point DWB.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-29 in stat.ME).
Categorie arXiv: Methodology (stat.ME) ; Econometrics (econ.EM).
Pagina del lavoro: https://arxiv.org/abs/2609.32369 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T22:41:46.583Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).