  The paper focuses on the adaptation of local polynomial filters at the end of
the sample period. We show that for real time estimation of signals (i.e.,
exactly at the boundary of the time support) we cannot rely on the automatic
adaptation of the local polynomial smoothers, since the direct real time filter
turns out to be strongly localized, and thereby yields extremely volatile
estimates. As an alternative, we evaluate a general family of asymmetric
filters that minimizes the mean square revision error subject to polynomial
reproduction constraints; in the case of the Henderson filter it nests the
well-known Musgrave's surrogate filters. The class of filters depends on
unknown features of the series such as the slope and the curvature of the
underlying signal, which can be estimated from the data. Several empirical
examples illustrate the effectiveness of our proposal.
