Model Selection when there are Multiple Breaks

  title={Model Selection when there are Multiple Breaks},
  author={Jennifer L. Castle and Jurgen A. Doornik and David F. Hendry},
We consider model selection facing uncertainty over the cho ice f variables and the occurrence and timing of multiple location shifts. General-to-simple selection is extended by adding an impulse indicator for every observation to the set of candidate regr essors: see Johansen and Nielsen (2009). We apply that approach to a fat-tailed distribution, and to p r cesses with breaks: Monte Carlo experiments show its capability of detecting up to 20 shifts in 100 observations, while jointly selecting… CONTINUE READING