  One of the most important hyper-parameters in duration-dependent
Markov-switching (DDMS) models is the duration of the hidden states. Because
there is currently no procedure for estimating this duration or testing whether
a given duration is appropriate for a given data set, an ad hoc duration choice
must be heuristically justified. In this paper, we propose and examine a
methodology that mitigates the choice of duration in DDMS models when
forecasting is the goal. Two Monte Carlo simulations, based on classical
applications of DDMS models, are employed to evaluate the methodology. In
addition, an empirical investigation is carried out to forecast the volatility
of the S\&P 500, which showcases the capabilities of the proposed model.
