Desirability of Nominal GDP Targeting under Adaptive Learning

Kaushik Mitra

    Research output: Contribution to journalArticlepeer-review

    9 Citations (Scopus)

    Abstract

    Nominal GDP targeting has been advocated by a number of authors since it produces relative stability of inflation and output. However, all of the papers assume rational expectations on the part of private agents. In this paper I provide an analysis of this assumption. I use stability under recursive learning as a criterion for evaluating nominal GDP targeting in the context of a model with explicit micro-foundations which is currently the workhorse for the analysis of monetary policy.

    Original languageEnglish
    Pages (from-to)197-220
    Number of pages24
    JournalJournal of Money, Credit and Banking
    Volume35
    Issue number2
    DOIs
    Publication statusPublished - Apr 2003

    Keywords

    • MONETARY-POLICY RULES
    • RATIONAL-EXPECTATIONS
    • INTEREST-RATES
    • STABILITY
    • MODELS
    • CONVERGENCE
    • PERSPECTIVE

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