Desirability of Nominal GDP Targeting Under Adaptive Learning
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.Download Info
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Paper provided by Department of Economics, University of York in its series Discussion Papers with number 00/60.Length:
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Handle: RePEc:yor:yorken:00/60
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Keywords: Nominal GDP; learning; expectational stability.;Other versions of this item:
- Mitra, Kaushik, 2003. " Desirability of Nominal GDP Targeting under Adaptive Learning," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 35(2), pages 197-220, April.
- E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
- E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit
This paper has been announced in the following NEP Reports:
- NEP-ALL-2001-01-21 (All new papers)
- NEP-MON-2001-01-21 (Monetary Economics)
References
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Heinz-Peter Spahn, 2004. "Learning in Macroeconomics and Monetary Policy: The Case of an Open Economy," Diskussionspapiere aus dem Institut für Volkswirtschaftslehre der Universität Hohenheim 236/2004, Department of Economics, University of Hohenheim, Germany.
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