Business cycle durations

While the development of Markov switching extensions to time series modeling has provided a useful way of characterizing business cycle dynamics, these models are not without their weaknesses. One problem is posed by the fact that since the state space for the unobserved state variables 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 phase is constant. This paper extends the model so that the information contained in leading indicator data can be used to forecast transition probabilities. These transition probabilities can then be used to calculate expected durations. The model is applied to US data to evaluate its ability to explain observed business cycle durations. The technical problems encountered with classical techniques are avoided by using Bayesian methods. Gibbs sampling techniques are used to calculate expected posterior durations.