Scenarios and policy aggregation in optimization under uncertainty
Mathematics of Operations Research
Approximate scenario solutions in the progressive hedging algorithm: a numerical study
Annals of Operations Research
Numerical Recipes: FORTRAN
Optimal Control and Stochastic Simulation of Large Nonlinear Models withRational Expectations
Computational Economics
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This paper considers the solution of nonlinear rationalexpectations models resulting from the optimality conditions of afinite-horizon intertemporal optimization problem satisfying Bellman'sprinciple of optimality (and possibly involving inequality constraints). Abackward recursive procedure is used to characterize and solve thetime-varying optimal decision rules generally associated with these models.At each stage of these backward recursions, either an analytical ornumerical solution of the optimality conditions is required. When ananalytical solution is not possible, a minimum weighted residual approach isused. The solution technique is illustrated using a life-cycle model ofconsumption under labor income and interest rate uncertainties (and possiblyinvolving liquidity constraints). Approximate numerical solutions areprovided and compared with certainty-equivalent solutions and, whenpossible, with exact solutions.