Global Stability in Spite of "Local Instability" with Learning in General Equilibrium Models: A Generalization
In this paper we identify a simple property of nonlinear temporary equilibrium map (TEM) that guarantees that all trajectories, along which the dynamics of the state variable remain bounded, converge to the steady state-in particular, the locally divergent trajactories also are driven back to the steady state. This property if used to show that one may get globally stable learning dynamics in spite of "locally unstable"behaviour in realistic economic models.
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