The Grid Bootstrap and the Autoregressive Model
提出一种网格自助法来构建置信区间，在自回归模型根接近单位根时比传统自助法更准确，模拟验证了其全局一阶正确性。
A "grid" bootstrap method is proposed for confidence-interval construction, which has improved performance over conventional bootstrap methods when the sampling distribution depends upon the parameter of interest. The basic idea is to calculate the bootstrap distribution over a grid of values of the parameter of interest and form the confidence interval by the no-rejection principle. Our primary motivation is given by autoregressive models, where it is known that conventional bootstrap methods fail to provide correct first-order asymptotic coverage when an autoregressive root is close to unity. In contrast, the grid bootstrap is first-order correct globally in the parameter space. Simulation results verify these insights, suggesting that the grid bootstrap provides an important improvement over conventional methods. Gauss code that calculates the grid bootstrap intervals - and replicates the empirical work reported in this paper - is available from the author's Web page at www.ssc.wisc.edu~bhansen. © 2000 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology