论文标题
分位数自注重和预测回归的单位根部中度偏差的渐近理论
Asymptotic Theory for Unit Root Moderate Deviations in Quantile Autoregressions and Predictive Regressions
论文作者
论文摘要
当指定模型参数相对于与形式的单位边界（1 + c / k）的中等偏差指定的模型参数时，我们建立了渐近理论，其收敛序列以比样本大小n慢的速率差异。然后，扩展了Phillips和Magdalinos（2007）提出的框架，我们考虑了近地阶层和近探索案例的极限理论，当模型估算为有条件的分位数规范函数和模型参数依赖于分数依赖性。此外，得出了基于模型参数的M估计器的Bahadur型表示和限制分布。具体而言，我们表明，串行相关系数在分布中收敛到两个独立随机变量的比率。蒙特卡洛模拟说明了正在研究的估计程序的有限样本性能。
We establish the asymptotic theory in quantile autoregression when the model parameter is specified with respect to moderate deviations from the unit boundary of the form (1 + c / k) with a convergence sequence that diverges at a rate slower than the sample size n. Then, extending the framework proposed by Phillips and Magdalinos (2007), we consider the limit theory for the near-stationary and the near-explosive cases when the model is estimated with a conditional quantile specification function and model parameters are quantile-dependent. Additionally, a Bahadur-type representation and limiting distributions based on the M-estimators of the model parameters are derived. Specifically, we show that the serial correlation coefficient converges in distribution to a ratio of two independent random variables. Monte Carlo simulations illustrate the finite-sample performance of the estimation procedure under investigation.
