Bootstrapping cointegrating regressions using blockwise bootstrap methods
The paper discusses the issue of block length selection in the blockwise bootstrap approaches to cointegrating regressions in small samples. The advantages in handling serially correlated errors are emphasized for the blockwise bootstrap approaches. Block length rules proposed in the previous literature are discussed. Some related Monte Carlo results are given as well to study how the estimation results are affected by the selection of block length in small samples.
cointegration size distortion moving block bootstrap stationary bootstrap