Two-sided Learning and Optimal Monetary Policy in an Open Economy Model

Timothy Kam

No 04-07, Economics Discussion / Working Papers from The University of Western Australia, Department of Economics

Abstract: In this paper, we consider a dynamic New Keynesian model of the small open economy in the light of bounded rationality. This entails private agents and the central bank updating their beliefs about the laws of motion of inflation, the output gap and real exchange rate, when forming their optimal decisions. It is shown that when all players learn using recursive least-squares or stochastic-gradient adaptive algorithms, the optimal policy steers the economy towards a rational expectations equilibrium (REE) with probability one in some cases. This is also the case when only private agents are learning. However there also exists structural parameter values in the true model such that learning converges with probability zero to REE.

Keywords: Optimal monetary policy; Small open economy; Learning; Stochastic approximation (search for similar items in EconPapers)
JEL-codes: D83 E52 F41 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://ecompapers.biz.uwa.edu.au/paper/PDF%20of%2 ... s/2004/04_07_Kam.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:uwa:wpaper:04-07

Access Statistics for this paper

More papers in Economics Discussion / Working Papers from The University of Western Australia, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sam Tang ().