An Alternative to Stationarization
For good reasons, it is standard practice to remove trends before linearizing a growth model. This paper explores an alternative strategy that consists in computing local approximations around successive points in the state space. Obviously this is terribly inefficiant it trend removal is feasible, but it makes possible to do local approximation in models that don't allow for trend removal. Among such models one can find models that don't display balanced growth and some models with learning
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