Uniform Inference in Autoregressive Models
为自回归模型中系数和的置信区间构建方法提供理论依据，证明Stock、Andrews和Hansen的方法渐近有效，而Romano和Wolf的子抽样方法无效，并澄清了统一与逐点渐近近似的区别。
The purpose of this paper is to provide theoretical justification for some existing methods for constructing confidence intervals for the sum of coefficients in autoregressive models. We show that the methods of Stock (1991), Andrews (1993), and Hansen (1999) provide asymptotically valid confidence intervals, whereas the subsampling method of Romano and Wolf (2001) does not. In addition, we generalize the three valid methods to a larger class of statistics. We also clarify the difference between uniform and pointwise asymptotic approximations, and show that a pointwise convergence of coverage probabilities for all values of the parameter does not guarantee the validity of the confidence set. Copyright The Econometric Society 2007.