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Central Bank Learning, Terms of Trade Shocks & Currency Risk: Should Exchange Rate Volatility Matter for Monetary Policy?

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Author Info

  • G.C. Lim

    (University of Melbourne)

  • Paul D. McNelis

    ()
    (Boston College)

Abstract

This paper examines the role of interest rate policy in a small open economy subject to terms of trade shocks and time-varying currency risk responding to domestic exchange rate volatility. The private sector makes optimal decisions in an intertemporal non-linear setting with rational,forward-looking expectations. In contrast,the monetary authority practices least-squares learning about the evolution of inflation, output growth, and exchange rate depreciation in alternative policy scenarios. Interest rates are set by linear quadratic optimization, with the objectives for inflation, output growth, or depreciation depending on current conditions. The simulation results show that the preferred stance is one which targets inflation and growth, not inflation only nor inflation, growth and depreciation. Including exchange rate changes as targets significantly increases output variability, but marginally reduces inflation variability.

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Bibliographic Info

Paper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 509.

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Length: 26 pages
Date of creation: 01 Oct 2001
Date of revision:
Handle: RePEc:boc:bocoec:509

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Keywords: Currency risk; learning; parameterized expectations; policy targets;

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  1. McCallum, Bennett T, 2000. "Theoretical Analysis Regarding a Zero Lower Bound on Nominal Interest Rates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 32(4), pages 870-904, November.
  2. Christopher J. Erceg & Dale W. Henderson & Andrew T. Levin, 1999. "Optimal monetary policy with staggered wage and price contracts," International Finance Discussion Papers 640, Board of Governors of the Federal Reserve System (U.S.).
  3. Judd, Kenneth L., 1996. "Approximation, perturbation, and projection methods in economic analysis," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 12, pages 509-585 Elsevier.
  4. Taylor, John B., 1993. "Discretion versus policy rules in practice," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 39(1), pages 195-214, December.
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  6. Lawrence J. Christiano & Christopher J. Gust, 1999. "Taylor Rules in a Limited Participation Model," NBER Working Papers 7017, National Bureau of Economic Research, Inc.
  7. Chris Ryan & Christopher Thompson, 2000. "Inflation Targeting and Exchange Rate Fluctuations in Australia," RBA Research Discussion Papers rdp2000-06, Reserve Bank of Australia.
  8. Duffy, John & McNelis, Paul D., 2001. "Approximating and simulating the stochastic growth model: Parameterized expectations, neural networks, and the genetic algorithm," Journal of Economic Dynamics and Control, Elsevier, vol. 25(9), pages 1273-1303, September.
  9. Mendoza, Enrique G, 1995. "The Terms of Trade, the Real Exchange Rate, and Economic Fluctuations," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 36(1), pages 101-37, February.
  10. Julio J. Rotemberg & Michael Woodford, 1998. "An Optimization-Based Econometric Framework for the Evaluation of Monetary Policy: Expanded Version," NBER Technical Working Papers 0233, National Bureau of Economic Research, Inc.
  11. Wright, Brian D & Williams, Jeffrey C, 1982. "The Economic Role of Commodity Storage," Economic Journal, Royal Economic Society, vol. 92(367), pages 596-614, September.
  12. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, Spring.
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