Markovian processes, two-sided autoregressions and finite-sample inference for stationary and nonstationary autoregressive processes
马尔可夫过程、双向自回归与平稳及非平稳自回归的有限样本推断
In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression…