Saddlepath learning, MSV learning and consistency of subjective expectations

Research output: Contribution to journalArticle

Abstract

Ellison and Pearlman (2011) show that determinacy implies e-stability under both full and lagged information if (1) subjective expectations are consistent with a structural model and unbiased, and (2) a learning process is given by the saddlepath relationship. This study clarifies that their equivalence result under any information set is a consequence of their assumptions about bounded rationality. The same result also holds for the standard MSV learning approach under consistency or unbiasedness assumption. Without these assumptions, the equivalence result no longer holds for either saddlepath or MSV learning.

Original languageEnglish
Pages (from-to)319-322
Number of pages4
JournalEconomics Letters
Volume125
Issue number2
DOIs
Publication statusPublished - 2014 Nov 1

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Equivalence
Subjective expectations
Learning process
Structural model
Unbiasedness
Bounded rationality
Determinacy

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics and Econometrics

Cite this

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title = "Saddlepath learning, MSV learning and consistency of subjective expectations",
abstract = "Ellison and Pearlman (2011) show that determinacy implies e-stability under both full and lagged information if (1) subjective expectations are consistent with a structural model and unbiased, and (2) a learning process is given by the saddlepath relationship. This study clarifies that their equivalence result under any information set is a consequence of their assumptions about bounded rationality. The same result also holds for the standard MSV learning approach under consistency or unbiasedness assumption. Without these assumptions, the equivalence result no longer holds for either saddlepath or MSV learning.",
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Saddlepath learning, MSV learning and consistency of subjective expectations. / Cho, Seonghoon.

In: Economics Letters, Vol. 125, No. 2, 01.11.2014, p. 319-322.

Research output: Contribution to journalArticle

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