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This article develops a new Markov breaks (MB) model for forecasting and making inference in regression models with stochastic breaks. The MB model permits an arbitrarily large number of abrupt breaks in the regression coefficients and error variance, but it maintains a low-dimensional state space, and therefore it is computationally straightforward. I compare the model to competing breaks models and show that it outperforms them in a Monte Carlo experiment. I employ the MB model to assess thedoi:10.1515/1941-1928.1111 fatcat:dxgtjr3qvve2vnrm4pt5ovytla