Outcome(x,t) = Trend(t,Treatment(x)) + Shocks(x,t)

And then hierarchically let the Shocks(x,t) be related between related occupations indexed by x, and inform the Shocks(x,t) functions by external data on shocks.

]]>*I know, you already put checking for a difference from control in your dissertation aims, that is your committee’s fault. Blame them, dont let them drag you down deeper into the hole they’ve dug for themselves.

]]>@Jonas: Do you have enough pre-treatment observations (time periods)? If so, I think you might be interested by the synthetic control method. It would construct the conterfactual as a weighted average of other types of occupations (where the weights are chosen so the counterfactual most closely resembles the treated occupation before the intervention). You could then argue the absence of specific shocks for the subset of occupations with weights different from zero. See David McKenzie on the Development Impact blog for a great illustration http://blogs.worldbank.org/impactevaluations/evaluating-regulatory-reforms-using-the-synthetic-control-method ]]>