Desirability of Nominal GDP Targeting Under Adaptive Learning

Kaushik Mitra ()

Discussion Papers from Department of Economics, University of York

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. The results of the paper provide support for such a monetary policy.

Keywords: Nominal GDP; learning; expectational stability. (search for similar items in EconPapers)
JEL-codes: E4 E5 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-mon
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Related works:
Journal Article: Desirability of Nominal GDP Targeting under Adaptive Learning (2003)
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