A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter
针对AR(1)模型的自回归参数，提出一种新的置信区间，允许一般形式的条件异方差且参数小于等于1，无需调节参数，蒙特卡洛模拟显示有限样本下覆盖概率和平均长度表现良好。
This paper introduces a new confidence interval (CI) for the autoregressive parameter (AR) in an AR(1) model that allows for conditional heteroskedasticity of a general form and AR parameters that are less than or equal to unity. The CI is a modification of Mikusheva's (2007a) modification of Stock's (1991) CI that employs the least squares estimator and a heteroskedasticity-robust variance estimator. The CI is shown to have correct asymptotic size and to be asymptotically similar (in a uniform sense). It does not require any tuning parameters. No existing procedures have these properties. Monte Carlo simulations show that the CI performs well in finite samples in terms of coverage probability and average length, for innovations with and without conditional heteroskedasticity. © 2014 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.