  In this short note I apply the methodology of game-theoretic probability to
calculating non-asymptotic confidence intervals for the coefficient of a simple
first order scalar autoregressive model. The most distinctive feature of the
proposed procedure is that with high probability it produces confidence
intervals that always cover the true parameter value when applied sequentially.
