Mikihito Nishi, Takashi Yamagata
arXiv 26 Sep 2026 · Statistics — Methodology
arXiv:2609.32369 · PDF · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Newey, Whitney K. and McFadden, Daniel (1994) Large Sample Estimation and Hypothesis Testing | 0.737 | 3 | 2 | 100% |
| 2 | Shao, Xiaofeng (2010) The Dependent Wild Bootstrap | 0.659 | 7 | 2 | 43% |
| 3 | Dai, Runyu and Matsushita, Yukitoshi and Yamagata, Takashi (2026) Two-Point Dependent Wild Bootstrap in Large Panels self | 0.644 | 2 | 2 | 100% |
| 4 | Andrews, Donald W. K (1991) Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation | 0.585 | 3 | 1 | 100% |
| 5 | Davidson, Russell and Flachaire, Emmanuel (2008) The Wild Bootstrap, Tamed at Last | 0.511 | 2 | 1 | 100% |
| 6 | Conley, Timothy G. and Gon calves, Sílvia and Kim, Min Seong and Per… (2023) Bootstrap Inference under Cross-Sectional Dependence | 0.405 | 1 | 1 | 100% |
| 7 | Cox, John C. and Ingersoll, Jonathan E. Jr. and Ross, Stephen A (1985) A Theory of the Term Structure of Interest Rates | 0.405 | 1 | 1 | 100% |
| 8 | Davidson, James and Monticini, Andrea and Peel, David (2007) Implementing the Wild Bootstrap Using a Two-Point Distribution | 0.405 | 1 | 1 | 100% |
| 9 | Emrich, Lawrence J. and Piedmonte, Marion R (1991) A Method for Generating High-Dimensional Multivariate Binary Variates | 0.405 | 1 | 1 | 100% |
| 10 | Gao, Jiti and Peng, Bin and Yan, Yayi (2024) Higher-Order Expansions and Inference for Panel Data Models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 24 scored citations.