Bootstrap-Based Inference in Models With a Nearly Noninvertible Moving Average Component
提出一种自举方法，用于在近不可逆移动平均模型中构建移动平均参数的双侧置信区间，通过反转似然比检验的接受域实现，并应用于通胀参数不稳定性和利率风险溢价时变性的研究。
This article proposes a bootstrap method for constructing two-sided confidence intervals for the moving average (MA) parameter in nearly noninvertible models. The confidence intervals are obtained by inverting the acceptance region of the likelihood ratio (LR) test reflecting the asymmetry of the likelihood near the noninvertibility boundary. The limiting distribution of the LR statistic is nonpivotal and its quantiles are parameterized as a function of the MA parameter and then approximated by grid bootstrap. The proposed method is used to investigate the parameter instability in inflation and time variability of risk premium in interest rates.