自助法修正的预白化HAC在平稳过程之间伪回归中的应用

传统预白化HAC法存在有限样本偏差,导致在相互独立的平稳过程回归中存在较高的伪回归概率.本文提出利用自助法减少其有限样本偏差,从而降低伪回归概率.同时,通过与一阶偏差修正的预白化HAC法的比较分析,发现自助法修正的预白化HAC不仅能减少偏差和降低伪回归概率,而且在GARCH类异方差和高阶自回归数据过程中具有更好的稳健性.

The application of bootstrap-correction prewhitening HAC method in spurious regression between stationary processes

The traditional prewhitening HAC methods have finite sample bias, leading to spurious regression probability is higher in regression models between independent stationary processes. This paper proposes to modify the traditional prewhitening HAC methods by using the bootstrap-correction method in the estimation of autoregressive coefficients, aims to reduce their finite sample bias, thus further reducing the possibility of spurious regression. At the same time, by comparing with the first-order bias-correction prewhitening HAC methods, we find the bootstrap-correction prewhitening HAC methods can not only reduce finite sample bias, but also obviously reduce spurious regression probability, and has better robustness than the first-order bias-correction methods under GARCH heteroscedasticity and higher-order autoregressive data generation processes.