  Autoregressive models are a class of time series models that are important in
both applied and theoretical statistics. Typically, inferential devices such as
confidence sets and hypothesis tests for time series models require nuanced
asymptotic arguments and constructions. We present a simple alternative to such
arguments that allow for the construction of finite sample valid inferential
devices, using a data splitting approach. We prove the validity of our
constructions, as well as the validity of related sequential inference tools. A
set of simulation studies are presented to demonstrate the applicability of our
methodology.
