Linear-Quadratic Approximation and Value-Function Iteration: A Comparison

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Abstract
This article studies the accuracy of two versions of Kydland and Prescott's (1980, 1982) procedure for approximating optimal decision rules in problems in which the objective fails to be quadratic and the constraints fail to be linear. The analysis is carried out using a version of the Brock–Mirman (1972) model of optimal economic growth. Although the model is not linear quadratic, its solution can, nevertheless, be computed with arbitrary accuracy using a variant of existing value-function iteration procedures. I find that the Kydland–Prescott approximate decision rules are very similar to those implied by value-function iteration.

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