An approximation of the distribution of learning estimates in macroeconomic models
Open access
Date
2019-03Type
- Working Paper
ETH Bibliography
yes
Altmetrics
Abstract
Adaptive learning under constant-gain allows persistent deviations of beliefs from equilibrium 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 understanding 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 dynamic macroeconomic models under constant-gain learning. Our approximation provides renewed convergence conditions that depend on the learning gain and the model’s structural parameters. We validate the accuracy of our approximation with numerical simulations 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 analysis of learning stability and the effects of alternative specifications of interest rate policy rules on the distribution of agents’ beliefs. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000332885Publication status
publishedJournal / series
KOF Working PapersVolume
Publisher
KOF Swiss Economic Institute, ETH ZurichSubject
Expectations; Adaptive learning; Constant-gain; Policy stabilityOrganisational unit
03716 - Sturm, Jan-Egbert / Sturm, Jan-Egbert
02525 - KOF Konjunkturforschungsstelle / KOF Swiss Economic Institute
Related publications and datasets
Is original form of: http://hdl.handle.net/20.500.11850/311798
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ETH Bibliography
yes
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