BUSINESS-CYCLE DURATIONS

While the development of Markov switching extensions to time series mo deling has provided a useful way of characterizing business cycle dyna mics, these models are not without their weaknesses. One problem is po sed by the fact that since the state space for the unobserved state va riables grows with the sample size, sampling distributions for maximum -likelihood estimates are difficult to establish. A second problem is that since the transition probabilities are constant, the conditional expected duration of a phase is constant. This paper extends the model so that the information contained in leading indicator data can be us ed to forecast transition probabilities. These transition probabilitie s can then be used to calculate expected durations. The model is appli ed to US data to evaluate its ability to explain observed business cyc le durations. The technical problems encountered with classical techni ques are avoided by using Bayesian methods. Gibbs sampling techniques are used to calculate expected posterior durations. (C) 1998 Elsevier Science S.A. All rights reserved.