A simple parameter‐driven binary time series model

We introduce a parameter‐driven, state‐space model for binary time series data. The model is based on a state process with a binomial‐beta dynamics, which has a Markov, endogenous switching regime representation. The model allows for recursive prediction and filtering formulas with extremely low computational cost, and hence avoids the use of computational intensive simulation‐based filtering algorithms. Case studies illustrate the advantage of our model over popular intensity‐based observation‐driven models, both in terms of fit and out‐of‐sample forecast.