Saddlepath learning.

Saddlepath learning occurs when agents know the form but not the coefficients of the saddlepath relationship defining rational expectations equilibrium. Under saddlepath learning, we obtain a completely general relationship between determinacy and e-stability, and generalise Minimum State Variable r...

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Main Authors: Ellison, M, Pearlman, J
Format: Journal article
Language:English
Published: Elsevier 2011
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author Ellison, M
Pearlman, J
author_facet Ellison, M
Pearlman, J
author_sort Ellison, M
collection OXFORD
description Saddlepath learning occurs when agents know the form but not the coefficients of the saddlepath relationship defining rational expectations equilibrium. Under saddlepath learning, we obtain a completely general relationship between determinacy and e-stability, and generalise Minimum State Variable results previously derived only under full information. When the system is determinate, we show that a learning process based on the saddlepath is always e-stable. When the system is indeterminate, we find there is a unique MSV solution that is iteratively e-stable. However, in this case there is a sunspot solution that is learnable as well. We conclude by demonstrating that our results hold for any information set.
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spelling oxford-uuid:6d4c53c2-600b-430e-8c18-a488156479152022-03-26T19:16:52ZSaddlepath learning.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:6d4c53c2-600b-430e-8c18-a48815647915EnglishDepartment of Economics - ePrintsElsevier2011Ellison, MPearlman, JSaddlepath learning occurs when agents know the form but not the coefficients of the saddlepath relationship defining rational expectations equilibrium. Under saddlepath learning, we obtain a completely general relationship between determinacy and e-stability, and generalise Minimum State Variable results previously derived only under full information. When the system is determinate, we show that a learning process based on the saddlepath is always e-stable. When the system is indeterminate, we find there is a unique MSV solution that is iteratively e-stable. However, in this case there is a sunspot solution that is learnable as well. We conclude by demonstrating that our results hold for any information set.
spellingShingle Ellison, M
Pearlman, J
Saddlepath learning.
title Saddlepath learning.
title_full Saddlepath learning.
title_fullStr Saddlepath learning.
title_full_unstemmed Saddlepath learning.
title_short Saddlepath learning.
title_sort saddlepath learning
work_keys_str_mv AT ellisonm saddlepathlearning
AT pearlmanj saddlepathlearning