The Grid Bootstrap for Continuous Time Models
针对连续时间模型中的持久参数，提出改进的网格自助法构建置信区间，解决了初始条件问题，并通过蒙特卡洛模拟和美国利率数据验证了其优于标准网格自助法。
This article proposes the new grid bootstrap to construct confidence intervals (CI) for the persistence parameter in a class of continuous-time models. It is different from the standard grid bootstrap of Hansen in dealing with the initial condition. The asymptotic validity of the CI is discussed under the in-fill scheme. The modified grid bootstrap leads to uniform inferences on the persistence parameter. Its improvement over in-fill asymptotics is achieved by expanding the coefficient-based statistic around its in-fill asymptotic distribution that is non-pivotal and depends on the initial condition. Monte Carlo studies show that the modified grid bootstrap performs better than Hansen’s grid bootstrap. Empirical applications to the U.S. interest rates and volatilities suggest significant differences between the two bootstrap procedures when the initial condition is large.