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Monetary Policy Rules with Model and Data Uncertainty

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Author Info
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)

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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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Publisher 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 listed on IDEAS
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Cited by:
(explanations, 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.)

  1. Peter Christoffersen & Eric Ghysels & Norman Swanson, 2000. "Let's Get "Real" About Using Economic Data," Econometric Society World Congress 2000 Contributed Papers 1004, Econometric Society. [Downloadable!]
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  2. Raffaella Giacomini & Barbara Rossi, 2006. "Detecting and predicting forecast breakdowns," Working Paper Series 638, European Central Bank. [Downloadable!]
    Other versions:
  3. Söderström, Ulf, 1999. "Should central banks be more aggressive?," Working Paper Series 84, Sveriges Riksbank (Central Bank of Sweden). [Downloadable!]
    Other versions:
  4. 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. [Downloadable!]
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