Abstract
We present an aggregative model of dynamic optimization under uncertainty and seek to synthesize and extend results that apply to a number of well-studied stochastic optimization exercises. First, the question of existence of a unique finite horizon optimal program is considered. Optimality is characterized in terms of Ramsey-Euler conditions. Sensitivity of the optimal programs with respect to changes in initial and terminal stocks is explored. As the horizon expands, the optimal programs converge to a unique limit program, which is linked to the infinite horizon optimal program. A turnpike property of optimal programs is also derived.
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