BOOTSTRAPPING UNSTABLE 1ST-ORDER AUTOREGRESSIVE PROCESSES
成果类型：
Note
署名作者：
BASAWA, IV; MALLIK, AK; MCCORMICK, WP; REEVES, JH; TAYLOR, RL
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
DOI：
10.1214/aos/1176348142
发表日期：
1991
页码：
1098-1101
关键词：
摘要：
Consider a first-order autoregressive process X(t) = beta-X(t-1) + epsilon-t, where {epsilon-t} are independent and identically distributed random errors with mean 0 and variance 1. It is shown that when beta = 1 the standard bootstrap least squares estimate of beta-is asymptotically invalid, even if the error distribution is assumed to be normal. The conditional limit distribution of the bootstrap estimate at beta = 1 is shown to converge to a random distribution.