Heteroskedastic cointegration
研究了误差项具有非平稳方差的协整回归模型的渐近估计与推断理论，发现最小二乘估计以T½速率一致，但假设检验需使用稳健协方差矩阵估计，且推断理论存在干扰参数。
This paper develops an asymptotic theory of estimation and inference in ‘cointegrated’ regression models with errors displaying nonstationary variances. Least squares estimates are shown to be consistent at a T12 rate. Hypothesis testing requires the use of a robust covariance matrix estimate, in contrast to earlier work on cointegrated regressions. The inference theory is not nuisance-free, but preliminary investigations indicate that approximation by the normal distribution may be adequate in practice.