IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this article or follow this journal

Monetary Policy Rules with Model and Data Uncertainty

  • 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)

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

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.

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

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

as
in new window

Handle: RePEc:sej:ancoec:v:69:2:y:2002:p:239-265
Contact details of provider: Web page: http://www.southerneconomic.org/

More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. 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.
  2. Andrew Levin & Volker Wieland & John C. Williams, 1998. "Robustness of Simple Monetary Policy Rules under Model Uncertainty," NBER Working Papers 6570, National Bureau of Economic Research, Inc.
  3. 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.
  4. Canova, Fabio, 1993. "Detrending and Business Cycle Facts," CEPR Discussion Papers 782, C.E.P.R. Discussion Papers.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Albert Marcet & Juan P. Nicolini, 2003. "Recurrent Hyperinflations and Learning," American Economic Review, American Economic Association, vol. 93(5), pages 1476-1498, December.
  11. 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.).
  12. Ben S. Bernanke & Ilian Mihov, 1995. "Measuring Monetary Policy," NBER Working Papers 5145, National Bureau of Economic Research, Inc.
  13. Athanasios Orphanides, 1998. "Monetary policy rules based on real-time data," Finance and Economics Discussion Series 1998-03, Board of Governors of the Federal Reserve System (U.S.).
  14. 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.
  15. 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.
  16. Woodford, Michael, 1986. "Learning to Believe in Sunspots," Working Papers 86-16, C.V. Starr Center for Applied Economics, New York University.
  17. Dean Croushore, 1993. "Introducing: the survey of professional forecasters," Business Review, Federal Reserve Bank of Philadelphia, issue Nov, pages 3-15.
  18. 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.
  19. Fuhrer, Jeffrey C & Moore, George R, 1995. "Monetary Policy Trade-offs and the Correlation between Nominal Interest Rates and Real Output," American Economic Review, American Economic Association, vol. 85(1), pages 219-39, March.
  20. Dale W. Henderson & Warwick J. McKibbin, 1993. "A comparison of some basic monetary policy regimes for open economies: implications of different degrees of instrument adjustment and wage persistence," International Finance Discussion Papers 458, Board of Governors of the Federal Reserve System (U.S.).
  21. Julio J. Rotemberg & Michael Woodford, 1999. "Interest Rate Rules in an Estimated Sticky Price Model," NBER Chapters, in: Monetary Policy Rules, pages 57-126 National Bureau of Economic Research, Inc.
  22. Arturo Estrella & Frederic S. Mishkin, 2000. "Rethinking the Role of NAIRU in Monetary Policy: Implications of Model Formulation and Uncertainty," NBER Working Papers 6518, National Bureau of Economic Research, Inc.
  23. 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.
  24. Eric Hansen, 1996. "Price level versus inflation rate targets in an open economy with overlapping wage contracts," Pacific Basin Working Paper Series 96-01, Federal Reserve Bank of San Francisco.
  25. 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.
  26. 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.).
  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. Bray, Margaret, 1982. "Learning, estimation, and the stability of rational expectations," Journal of Economic Theory, Elsevier, vol. 26(2), pages 318-339, April.
  29. Robert G. King & Alexander L. Wolman, 1996. "Inflation targeting in a St. Louis model of the 21st century," Review, Federal Reserve Bank of St. Louis, issue May, pages 83-107.
  30. Taylor, John B, 1979. "Estimation and Control of a Macroeconomic Model with Rational Expectations," Econometrica, Econometric Society, vol. 47(5), pages 1267-86, September.
  31. Ghysels, E., 1990. "On the Economic and Econometrics of Seasonality," Cahiers de recherche 9028, Universite de Montreal, Departement de sciences economiques.
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. Swanson, Norman R., 1998. "Money and output viewed through a rolling window," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 455-474, May.
  38. 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.
  39. 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.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:sej:ancoec:v:69:2:y:2002:p:239-265. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Laura Razzolini)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.