An approximation of the distribution of learning estimates in macroeconomic models

Galimberti, Jaqueson K.,(2019), An approximation of the distribution of learning estimates in macroeconomic models. , Journal of Economic Dynamics & Control, UNSPECIFIED

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Adaptive learning under constant-gain allows persistent deviations of beliefs from equilib- rium so as to more realistically reflect agents’ attempt of tracking the continuous evolution of the economy. A characterization of these beliefs is therefore paramount to a proper un- derstanding of the role of expectations in the determination of macroeconomic outcomes. In this paper we propose a simple approximation of the first two moments (mean and variance) of the asymptotic distribution of learning estimates for a general class of dy- namic macroeconomic models under constant-gain learning. Our approximation provides renewed convergence conditions that depend on the learning gain and the model’s struc- tural parameters. We validate the accuracy of our approximation with numerical simu- lations of a Cobweb model, a standard New-Keynesian model, and a model including a lagged endogenous variable. The relevance of our results is further evidenced by an anal- ysis of learning stability and the effects of alternative specifications of interest rate policy rules on the distribution of agents’ beliefs.
Keywords : Expectations Adaptive learning constant-gain Policy stability, UNSPECIFIED
Journal or Publication Title: Journal of Economic Dynamics & Control
Volume: 102
Item Type: Article
Subjects: Ekonomi Pembangunan
Depositing User: Elok Inajati
Date Deposited: 30 Dec 2019 01:17
Last Modified: 30 Dec 2019 01:17

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