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An approximation of the distribution of learning estimates in macroeconomic models

Jaqueson Galimberti ()

No 19-453, KOF Working papers from KOF Swiss Economic Institute, ETH Zurich

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.

Keywords: expectations; adaptive learning; constant-gain; policy stability (search for similar items in EconPapers)
JEL-codes: D84 E03 E37 C62 C63 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2019-03
New Economics Papers: this item is included in nep-cmp, nep-dge, nep-mac and nep-ore
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