The jackknife and regression with AR(1) errors

In the linear regression model with strongly serially correlated errors, conventional methods often underestimate the standard errors, leading to excessive type I errors. A brief Monte Carlo study is presented showing that an application of Quenouille's half sample jackknife can reduce the type I error proportion substantially. Results using the bootstrap are also presented.