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Robust Stability of Monetary Policy Rules under Adaptive Learning
Author(s) -
Gaus Eric
Publication year - 2013
Publication title -
southern economic journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.762
H-Index - 58
eISSN - 2325-8012
pISSN - 0038-4038
DOI - 10.4284/0038-4038-2012.071
Subject(s) - minor (academic) , stability (learning theory) , variety (cybernetics) , adaptive learning , monetary policy , economics , subject (documents) , computer science , econometrics , artificial intelligence , machine learning , macroeconomics , law , political science , library science
Recent research has explored how minor changes in expectation formation can change the stability properties of a model ([Duffy, John,, 2007]; [Evans, George W.,, 2009]). This article builds on this research by examining an economy subject to a variety of monetary policy rules under an endogenous learning algorithm proposed by [Marcet, Albert,, 2003]. The results indicate that operational versions of optimal discretionary rules are not robustly stable, as in [Evans, George W.,, 2009]. In addition, commitment rules are not robust to minor changes in expectational structure and parameter values.