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A Method for Stochastic Control of Nonlinear Econometric Models and an Application
J. Phillip Cooper
Stanley Fishe
Abstract
A method for optimization in nonlinear stochastic models is proposed in this paper, and applied to study monetary and fiscal policy in the St. Louis econometric model. Essentially, the method is to simulate the model in a number of stochastic simulations in which the co-efficients of the model are treated as random, to use the results of the stochastic simulations to find parsimonious representations of the time form of the policy multipliers by estimating autoregressive moving-average regressions for the effects of policy on the relevant endogenous variables, and then to use these equations in computing optimal policy. The method is applied to the study of monetary and fiscal policy for the St. Louis model over a 60-period horizon with encouraging and sensible results.
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