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Monetary Policy Rules with Model and Data Uncertainty

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  • Eric Ghysels

    ()
    (Department of Economics, University of North Carolina at Chapel Hill)

  • Norman R. Swanson

    ()
    (Department of Economics, Rutgers University)

  • Myles Callan

    ()
    (Department of Economics, Clark University)

Abstract

In this paper we examine the prevalence of data, specification, and parameter uncertainty in the formation of simple rules that mimic monetary policymaking decisions. Our approach is to build real-time data sets and simulate a real-time policy-setting environment in which we assume that policy is captured by movements in the actual federal funds rate, and then to assess what sorts of policy rule models and what sorts of data best explain what the Federal Reserve actually did. This approach allows us not only to track the performance of alternative rules over time (hence facilitating a type of model selection among competing rules), but also to more generally assess the importance of the data revision process in the formation of macroeconomic time series models. From the perspective of real-time data, our results suggest that the use of data that are erroneous, in the sense that they were not available at the time decisions could have been made based on forecasts from the rules, can lead to the selection of quantitatively different models. From the perspective of finding a rule that best approximates what the Federal Reserve Board (Fed) has actually done (and hence from the perspective of finding a rule that best approximates what the Fed will do in the future), we find that (i) our version of “calibration” is better than naïve estimation, although both are dominated by an approach to rule formation based on the use of adaptive least-squares learning; (ii) rules based on data that are not seasonally adjusted are more reliable than those based on seasonally adjusted data; and (iii) rules based solely on preliminary data do not minimize mean square forecast error risk. In particular, early releases of data can be noisy, and for this reason it is useful to also use data that have been revised when making decisions using policy rules. We thank Dean Croushore, Lars Hansen, Glenn Rudebusch

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Bibliographic Info

Article provided by Southern Economic Association in its journal Southern Economic Journal.

Volume (Year): 69 (2002)
Issue (Month): 2 (October)
Pages: 239-265

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Handle: RePEc:sej:ancoec:v:69:2:y:2002:p:239-265

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References

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  1. Dean Croushore, 1993. "Introducing: the survey of professional forecasters," Business Review, Federal Reserve Bank of Philadelphia, issue Nov, pages 3-15.
  2. Clive W. J. Granger & Melinda Deutsch, 1991. "Comments on the evaluation of policy models," International Finance Discussion Papers 413, Board of Governors of the Federal Reserve System (U.S.).
  3. Julio J. Rotemberg & Michael Woodford, 1998. "Interest-Rate Rules in an Estimated Sticky Price Model," NBER Working Papers 6618, National Bureau of Economic Research, Inc.
  4. Norman R. Swanson & Halbert White, 1995. "A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks," Macroeconomics 9503004, EconWPA.
  5. Jeffrey A. Frankel and Menzie Chinn., 1991. "The Stabilizing Properties of a Nominal GNP Rule in an Open Economy," Economics Working Papers 91-166, University of California at Berkeley.
  6. Ghysels, E., 1990. "On The Economic And Econometrics Of Seasonality," Cahiers de recherche 9028, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  7. Frankel, Jeffrey, 1995. "The Stabilizing Properties of a Nominal GNP Rule," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 27(2), pages 318-34, May.
  8. Arturo Estrella & Frederic Mishkin, 1998. "Rethinking the role of NAIRU in monetary policy: implications of model formulation and uncertainty," Research Paper 9806, Federal Reserve Bank of New York.
  9. Taylor, John B, 1979. "Estimation and Control of a Macroeconomic Model with Rational Expectations," Econometrica, Econometric Society, vol. 47(5), pages 1267-86, September.
  10. Maravall, Agustin & Pierce, David A, 1986. "The Transmission of Data Noise into Policy Noise in U.S. Monetary Control," Econometrica, Econometric Society, vol. 54(4), pages 961-79, July.
  11. McCallum, Bennett T., 1999. "Issues in the design of monetary policy rules," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 23, pages 1483-1530 Elsevier.
  12. Kuan, Chung-Ming & White, Halbert, 1994. "Adaptive Learning with Nonlinear Dynamics Driven by Dependent Processes," Econometrica, Econometric Society, vol. 62(5), pages 1087-1114, September.
  13. Julio Rotemberg & Michael Woodford, 1997. "An Optimization-Based Econometric Framework for the Evaluation of Monetary Policy," NBER Chapters, in: NBER Macroeconomics Annual 1997, Volume 12, pages 297-361 National Bureau of Economic Research, Inc.
  14. Andrew Levin & Volker Wieland & John C. Williams, 1998. "Robustness of simple monetary policy rules under model uncertainty," Finance and Economics Discussion Series 1998-45, Board of Governors of the Federal Reserve System (U.S.).
  15. Henderson, Dale W. & McKibbin, Warwick J., 1993. "A comparison of some basic monetary policy regimes for open economies: implications of different degrees of instrument adjustment and wage persistence," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 39(1), pages 221-317, December.
  16. Ben S. Bernanke & Ilian Mihov, 1995. "Measuring Monetary Policy," NBER Working Papers 5145, National Bureau of Economic Research, Inc.
  17. Swanson, Norman R & White, Halbert, 1995. "A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 265-75, July.
  18. Swanson, Norman R., 1998. "Money and output viewed through a rolling window," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 455-474, May.
  19. Ghysels, Eric, 1987. "Seasonal Extraction in the Presence of Feedback," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(2), pages 191-94, April.
  20. Robert G. King & Alexander L. Wolman, 1996. "Inflation targeting in a St. Louis model of the 21st century," Proceedings, Federal Reserve Bank of St. Louis, issue May, pages 83-107.
  21. Bernanke, Ben S & Blinder, Alan S, 1992. "The Federal Funds Rate and the Channels of Monetary Transmission," American Economic Review, American Economic Association, vol. 82(4), pages 901-21, September.
  22. Marcet, A. & Nicolini, J.P., 1997. "Recurrent Hyperinflations and Learning," Papers 9721, Centro de Estudios Monetarios Y Financieros-.
  23. Marcet, Albert & Sargent, Thomas J., 1989. "Convergence of least squares learning mechanisms in self-referential linear stochastic models," Journal of Economic Theory, Elsevier, vol. 48(2), pages 337-368, August.
  24. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-89, June.
  25. McCallum, Bennett T., 1993. "Discretion versus policy rules in practice: two critical points : A comment," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 39(1), pages 215-220, December.
  26. Woodford, Michael, 1986. "Learning to Believe in Sunspots," Working Papers 86-16, C.V. Starr Center for Applied Economics, New York University.
  27. Trivellato, Ugo & Rettore, Enrico, 1986. "Preliminary Data Errors and Their Impact on the Forecast Error of Simultaneous-Equations Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 445-53, October.
  28. Francis X. Diebold & Glenn D. Rudebusch, 1989. "Forecasting output with the composite leading index: an ex ante analysis," Finance and Economics Discussion Series 90, Board of Governors of the Federal Reserve System (U.S.).
  29. Bray, Margaret, 1982. "Learning, estimation, and the stability of rational expectations," Journal of Economic Theory, Elsevier, vol. 26(2), pages 318-339, April.
  30. Marcet, Albert & Sargent, Thomas J, 1989. "Convergence of Least-Squares Learning in Environments with Hidden State Variables and Private Information," Journal of Political Economy, University of Chicago Press, vol. 97(6), pages 1306-22, December.
  31. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-52, April.
  32. Maravall, Agustin & Pierce, David A, 1983. "Preliminary-Data Error and Monetary Aggregate Targeting," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(3), pages 179-86, July.
  33. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
  34. Leitch, Gordon & Tanner, J Ernest, 1991. "Economic Forecast Evaluation: Profits versus the Conventional Error Measures," American Economic Review, American Economic Association, vol. 81(3), pages 580-90, June.
  35. 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.
  36. Canova, Fabio, 1993. "Detrending and Business Cycle Facts," CEPR Discussion Papers 782, C.E.P.R. Discussion Papers.
  37. Sargent, Thomas J, 1989. "Two Models of Measurements and the Investment Accelerator," Journal of Political Economy, University of Chicago Press, vol. 97(2), pages 251-87, April.
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