Using an evolving criterion to assess the Federal Reserve's behaviour in recent years
DOI:
https://doi.org/10.13133/2037-3643/9859Keywords:
Monetary Policy, Monetary, PolicyAbstract
The aim of this paper is to analyse the behaviour of the Federal Reserve in the Greenspan era by using recently developed neuro-fuzzy techniques. Such models require the operation assumptions concerning the conduct of monetary policy to be set in the form of flexible rules. Moreover, the approach allows an adaptive model to be built, thus pointing out the role played by the evolution of the monetary policy decision mechanism. Besides the usual set of macro-economic variables, the input data set includes a stock market indicator. Our results show that the simulated series of federal funds rate mimics almost perfectly the actual behaviour of the monetary policy instrument.
JEL Codes: E52, E58
References
ANGELOV P.P. and D.P. FILEV (2004), "An approach to online identification of Takagi-Sugeno fuzzy models", IEEE Transaction on Systems, Man and Cybernetics - Pan B: Cybernetics, vol. 34, no. 1, pp. 484-98.
CHIU, S.L. (1994), "Fuzzy model identification based on cluster estimation", journal of Intelligent and Fuzzy Systems, voL 2, no. 3, pp. 267-78.
CHRISTIANO, L., M. EICHENBAUM and C.L. EVANS (1999), "Monetary policy shocks: what have we learned and to what end?", in J-B. Taylor and M. Woodford eds, Handbook of Macroeconomics, North Holland, Amsterdam, pp. 65-148.
CLARIDA , R., J. GALÌ and M. GERTLER (1998), "Monetary policy rules in practice: some international evidence", European Economic Review, vol. 42, no. 6, pp. 1033-67.
CZOGALA, E. and J. LESKY (2000), Fuzzy and Neuro-Fuzzy Intelligent Systems, PhysicaVerlag, Heidelberg and New Y ork.
HADJILI, M.L. and V. WERTZ (2002), "Takagi-Sugeno fuzzy modeling incorporating input variables selection", IEEE Transaction on Fuzzy Systems, vol. lO, no. 6, pp. 728-42.
JANG, J.-S.R. (1993), "ANFIS: adaptive-network-based fuzzy inference systems", IEEE Transaction on Systems, Man and Cybernetics, vol. 23, no. 5, pp. 665-85.
JANG, J.-S.R. and N. GULLEY (1995), The Fuzzy Logic Toolbox for Use with Matlab, The Mathworks Inc., Natick.
JANG, J.-S.R., C.-T. SUN and E. MIZUTANI (1997), Neuro-Fuzzy and Soft Computing. A Computational Approach to Learning and Machine Intelligence, Prentice Hall, Upper Saddle River.
ORPHANIDES, A. (2001), "Monetary policy rules based on real-time data", American Economic Review, vol. 91, no. 4, pp. 964-85.
RIGOBON, R. and B. SACK (2003), "Measuring the reaction of monetary policy to the stock market", Quarterly Journal of Economics, vol. 118, pp. 639-69.
STAIGER D., J. STOCK and M. WATSON (1997), "The NAIRU, unemployment and monetary policy", Journal of Economic Perspectives, vol. 11, no. 1, pp. 33-49.
TAKAGI, T. and M. SUGENO (1985), "Fuzzy identification systems and its application to modeling and control", IEEE Transaction on Systems, Man and Cybernetics, vol. 15, no. 1, pp. 116-32.
TAYLOR, J. (1993), "Discretion versus policy rules in practice", Carnegie-Rochester Conference Series on Public Policy, vol. 39; pp. 195-214.
ZIEMMERMANN H.-J. (1996), Fuzzy Set Theory and its Applications, Kluwer Academic Publishers, Norwell.
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