发表机构
University of Tokyo; University of York; Tohoku University（东京大学; 约克大学; 东北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种两点依赖野生自助法，通过分离两点分布与序列相依设定，实现弱相依估计方程的有效推断，并在GMM与回归中验证其有限样本优势。
AI 中文摘要
本文针对弱相依估计方程，提出了一种通用的两点依赖野生自助法（DWB）。其关键特征在于，两点边际分布与潜在序列相依设定可以分别选择。该构造通过高斯连接函数变换，将归一化的两点分布与平稳潜在高斯过程相结合，包含相依的Rademacher和Mammen乘子，并将经典的独立同分布两点野生自助法作为序列独立情形纳入其中。由此产生的乘子自协方差决定了相应的异方差性和自相关一致性（HAC）协方差估计量中的滞后权重，该估计量与自助估计方程和的条件协方差完全一致。我们通过证明匹配的HAC估计量一致地估计长期协方差，且自助估计方程和条件收敛于与其原始样本对应项相同的高斯极限，建立了渐近线性估计量的一阶自助有效性，从而得到有效的HAC学生化z检验以及相应的Wald和拉格朗日乘子检验。在非线性广义矩方法（GMM）和线性回归中的蒙特卡洛实验表明，对于z检验，Rademacher DWB通常比Mammen和Gaussian DWB提供更精确的有限样本尺寸控制。一个应用于非线性短期利率均值回归模型的GMM实例，说明了所提出的两点DWB的实际相关性。
英文摘要
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.
