Resampling methods for tests in regression models with autocorrelated errors

摘要: Abstract This paper presents the results of a Monte Carlo study which suggest that bootstrap — in combination with bias-reduction method such as half-sample jackknife substantially corrects problem small and moderate samples excessive Type I error probabilities tests on coefficients regression models serially correlated disturbances. The methods are likely to be applicable testing many other situations.