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Monetary Policy Rules with Model and Data Uncertainty

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Author Info
Myles Callan
Eric Ghysels ()
Norman R. Swanson

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Abstract

We examine the prevalence of data, specification, and parameter uncertainty in the formation of simple rules which mimic monetary policy-making decisions. Our approach is to build real-time datasets, simulate a real-time policy-setting environment, and provide a set of prescriptions and diagnoses which are useful not only within the context on monetary policy rules, but also within the context of the application of real-time data to macroeconomics in general. Some of our findings can be summarized as follows. First, while our version of calibration is better than naive estimation, both are dominated by an approach to rule formation based on adaptive least squares learning using real-time data. Second, it appears that rules based on seasonally unadjusted data are more reliable than when seasonally adjusted data are used. Finally, it does not pay to use data which are too preliminary. Indeed, it appears that it would be in the best interest of policymakers to wait until some of the data uncertainty associated with preliminary data has been removed by the revision process. Although some rules require more patience than others, a prescription based on our best-performing rule points to a waiting period of 9 months for monthly data, which in turn leads to around a 50% increase in precision.

Nous étudions l'impact de l'incertitude par rapport aux données, la spécification du modèle ainsi que les paramètres sur des règles de décisions de politique monétaire. Notre analyse est fondée sur le modèle de Taylor et les règles de politique monétaire qui en découlent. Nous utilisons une banque de données qui contient l'historique des données macro-économique telles qu'elles ont été publiées et révisées à travers le temps. Ainsi notre étude est en temps réel et respecte la chronologie des données que les protagonistes de la politique avaient à leur disposition à travers le temps. Nous étudions différents mécanismes de calibrage et d'apprentisage par moyen d'estimation.

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Paper provided by CIRANO in its series CIRANO Working Papers with number 98s-40.

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Date of creation: 01 Nov 1998
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Handle: RePEc:cir:cirwor:98s-40

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Related research
Keywords: Data revision process; monetary authority credibility; predictive ability; adaptive and rational expectations; real-time data; Révision des données; crédibilité des politiques monétaires; attentes rationelles et adaptives; données en temps réel;

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Find related papers by JEL classification:
C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

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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. Raffaella Giacomini & Barbara Rossi, 2006. "Detecting and predicting forecast breakdowns," Working Paper Series 638, European Central Bank. [Downloadable!]
    Other versions:
  2. 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!]
    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!]
    Other versions:
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