Finite sample inference for generic autoregressive models
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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.
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