论文标题
动态面板阈值模型的引导程序
Bootstraps for Dynamic Panel Threshold Models
论文作者
论文摘要
本文为动态短面板阈值回归开发了有效的引导推理方法。我们证明标准的非参数bootstrap对于最初描述的矩（GMM）估计量的广义方法不一致。不一致是源于$ n^{1/4} $ - 当真实参数位于参数空间的连续性区域时，阈值估计器的一致的非正常渐近分布，这源于连续性区域样本矩条件的近似雅各布的等级。为了解决这个问题，我们提出了一个网格引导程序，以构建阈值的置信区间，并提出一个残留的bootstrap，以构建系数的置信区间。无论模型的连续性如何，它们都被证明是有效的。此外，我们为电网引导程序建立了统一的有效性。一组蒙特卡洛实验表明，所提出的引导程序在标准的非参数引导程序上有所改善。对公司投资模型的经验应用说明了我们的方法。
This paper develops valid bootstrap inference methods for the dynamic short panel threshold regression. We demonstrate that the standard nonparametric bootstrap is inconsistent for the first-differenced generalized method of moments (GMM) estimator. The inconsistency arises from an $n^{1/4}$-consistent non-normal asymptotic distribution of the threshold estimator when the true parameter lies in the continuity region of the parameter space, which stems from the rank deficiency of the approximate Jacobian of the sample moment conditions on the continuity region. To address this, we propose a grid bootstrap to construct confidence intervals for the threshold and a residual bootstrap to construct confidence intervals for the coefficients. They are shown to be valid regardless of the model's continuity. Moreover, we establish a uniform validity for the grid bootstrap. A set of Monte Carlo experiments demonstrates that the proposed bootstraps improve upon the standard nonparametric bootstrap. An empirical application to a firm investment model illustrates our methods.
