SUBSAMPLING FOR HETEROSKEDASTIC TIME-SERIES

In this article, a general theory for the construction of confidence i ntervals or regions in the context of heteroskedastic-dependent data i s presented. The basic idea is to approximate the sampling distributio n of a statistic based on the Values of the statistic computed over sm aller subsets of the data. This method was first proposed by Politis a nd Romano (1994b) for stationary observations. We extend their results to heteroskedastic observations, and prove a general asymptotic valid ity result under minimal conditions. In contrast, the usual bootstrap and moving blocks bootstrap are typically valid only for asymptoticall y linear statistics and their justification requires a case-by-case an alysis. Our general asymptotic results are applied to a regression set ting with dependent heteroskedastic errors. (C) 1997 Elsevier Science S.A.