This work deals with infinite horizon optimal growth models and uses the results in one of Mercenier and Michel (1994) papers as a starting point. Mercenier and Michel (1994) provided a one-stage Runge–Kutta discretization of the above-mentioned models which preserves the steady state of the theoretical solution. They call this feature the ‘‘steadystate invariance property’’. We generalize the result of their study by considering discrete models arising from the adoption of s-stage Runge–Kutta schemes. We show that the steady-state invariance property requires two different Runge–Kutta schemes for approximating the state variables and the exponential term in the objective function. This kind of discretization is well-known in literature as a partitioned symplectic Runge–Kutta scheme. Its main consequence is that it is possible to rely on the well-stated theory of order for considering more accurate methods which generalize the first order Mercenier and Michel algorithm. Numerical examples show the efficiency and accuracy of the proposed methods up to the fourth order, when applied to test models.

Steady-state invariance in high-order Runge-Kutta discretization of optimal growth models

RAGNI, Stefania;
2010

Abstract

This work deals with infinite horizon optimal growth models and uses the results in one of Mercenier and Michel (1994) papers as a starting point. Mercenier and Michel (1994) provided a one-stage Runge–Kutta discretization of the above-mentioned models which preserves the steady state of the theoretical solution. They call this feature the ‘‘steadystate invariance property’’. We generalize the result of their study by considering discrete models arising from the adoption of s-stage Runge–Kutta schemes. We show that the steady-state invariance property requires two different Runge–Kutta schemes for approximating the state variables and the exponential term in the objective function. This kind of discretization is well-known in literature as a partitioned symplectic Runge–Kutta scheme. Its main consequence is that it is possible to rely on the well-stated theory of order for considering more accurate methods which generalize the first order Mercenier and Michel algorithm. Numerical examples show the efficiency and accuracy of the proposed methods up to the fourth order, when applied to test models.
2010
Ragni, Stefania; Fasma, Diele; Carmela, Marangi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2336459
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