Desirability of Nominal GDP Targeting under Adaptive Learning

Kaushik Mitra

doi:doi:10.1353/mcb.2003.0014

Desirability of Nominal GDP Targeting under Adaptive Learning

Kaushik Mitra

Research output: Contribution to journal › Article › peer-review

11 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 language	English
Pages (from-to)	197-220
Number of pages	24
Journal	Journal of Money, Credit and Banking
Volume	35
Issue number	2
DOIs	https://doi.org/doi:10.1353/mcb.2003.0014
Publication status	Published - Apr 2003

Keywords

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

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doi:10.1353/mcb.2003.0014

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KW - CONVERGENCE

KW - PERSPECTIVE

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Desirability of Nominal GDP Targeting under Adaptive Learning

Abstract

Keywords

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