Finite sample inference for generic autoregressive models

Autoregressive stationary processes are fundamental modeling tools in time series analysis. To conduct inference for such models usually requires asymptotic limit theorems. We establish finite sample-valid tools for hypothesis testing and confidence set construction in such settings. Further results are established in the always-valid and sequential inference framework.