An information-theoretic lower bound in time-uniform estimation

We present an information-theoretic lower bound for the problem of parameter estimation with time-uniform coverage guarantees. Via a new a reduction to sequential testing, we obtain stronger lower bounds that capture the hardness of the time-uniform setting. In the case of location model estimation, logistic regression, and exponential family models, our $\mathrm{\Omega }(\sqrt{n^{-1}\log \log n})$ lower bound is sharp to within constant factors in typical settings.