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Monetary Policy Rules with Model and Data Uncertainty

Author

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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

Suggested Citation

  • Eric Ghysels & Norman R. Swanson & Myles Callan, 2002. "Monetary Policy Rules with Model and Data Uncertainty," Southern Economic Journal, Southern Economic Association, vol. 69(2), pages 239-265, October.
  • Handle: RePEc:sej:ancoec:v:69:2:y:2002:p:239-265
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    Cited by:

    1. Christoffersen, Peter & Ghysels, Eric & Swanson, Norman R., 2002. "Let's get "real" about using economic data," Journal of Empirical Finance, Elsevier, vol. 9(3), pages 343-360, August.
    2. Andres Fernandez & Norman R. Swanson, 2009. "Real-time datasets really do make a difference: definitional change, data release, and forecasting," Working Papers 09-28, Federal Reserve Bank of Philadelphia.
    3. Vázquez, Jesús & María-Dolores, Ramón & Londoño, Juan M., 2012. "The Effect of Data Revisions on the Basic New Keynesian Model," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 235-249.
    4. Ruben Atoyan & Patrick Conway, 2011. "Projecting macroeconomic outcomes: Evidence from the IMF," The Review of International Organizations, Springer, vol. 6(3), pages 415-441, September.
    5. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    6. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April.
    7. Akram, Q. Farooq, 2011. "Policy analysis in real time using IMF's monetary model," Economic Modelling, Elsevier, vol. 28(4), pages 1696-1709, July.
    8. Söderström, Ulf, 1999. "Should central banks be more aggressive?," Working Paper Series 84, Sveriges Riksbank (Central Bank of Sweden).
    9. Seth Pruitt, 2012. "Uncertainty Over Models and Data: The Rise and Fall of American Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44, pages 341-365, March.
    10. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, August.
    11. Felipe Morandé & Mauricio Tejada, 2008. "Sources of Uncertainty for Conducting Monetary Policy in Chile," Working Papers Central Bank of Chile 492, Central Bank of Chile.
    12. Vázquez, Jesús & María-Dolores, Ramón & Londoño, Juan-Miguel, 2013. "On the informational role of term structure in the US monetary policy rule," Journal of Economic Dynamics and Control, Elsevier, vol. 37(9), pages 1852-1871.
    13. Dean Croushore & Tom Stark, 2002. "Is macroeconomic research robust to alternative data sets?," Working Papers 02-3, Federal Reserve Bank of Philadelphia.
    14. Dean Croushore & Tom Stark, 2000. "A real-time data set for macroeconomists: does data vintage matter for forecasting?," Working Papers 00-6, Federal Reserve Bank of Philadelphia.
    15. Raffaella Giacomini & Barbara Rossi, 2009. "Detecting and Predicting Forecast Breakdowns," Review of Economic Studies, Oxford University Press, vol. 76(2), pages 669-705.
    16. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    17. Guido Bulligan & Roberto Golinelli & Giuseppe Parigi, 2010. "Forecasting industrial production: the role of information and methods," IFC Bulletins chapters,in: Bank for International Settlements (ed.), The IFC's contribution to the 57th ISI Session, Durban, August 2009, volume 33, pages 227-235 Bank for International Settlements.
    18. María-Dolores, Ramon & Vazquez, Jesus & Londoño, Juan M., 2009. "Extending the New Keynesian Monetary Model with Information Revision Processes: Real-time and Revised Data," UMUFAE Economics Working Papers 4695, DIGITUM. Universidad de Murcia.
    19. Guido Bulligan & Roberto Golinelli & Giuseppe Parigi, 2010. "Forecasting monthly industrial production in real-time: from single equations to factor-based models," Empirical Economics, Springer, vol. 39(2), pages 303-336, October.
    20. Felipe Morandé L. & Mauricio Tejada G., 2008. "Sources of Uncertainty in Monetary Policy Conduct in Chile," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 11(3), pages 45-80, December.

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    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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