Model Selection in Under-specified Equations Facing Breaks

Although a general unrestricted model may under-specify the data generation process, especially when breaks occur, model selection can still improve over estimating a prior specification.  Impulse-indicator saturation (IIS) can 'correct' non-constant intercepts induced by location shifts in omitted variables, which surprisingly leave slope parameters unaltered even when correlated with included variables.  However, location shifts in included variables do induce changes in slopes where there are correlated omitted variables.  IIS acts as a 'robust method' when models are mis-specified, and helps mitigate the adverse impacts of induced location shifts on non-constant intercepts and equation standard errors.