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Uncertainty over models and data: the rise and fall of American inflation

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  • Seth Pruitt

Abstract

An economic agent who is uncertain of her model updates her beliefs in response to the data. The updating is sensitive to measurement error which, in many cases of macroeconomic interest, is apparent from the process of data revision. I make this point through simple illustrations and then analyze a recent model of the Federal Reserve's role in U.S. inflation. The existing model succeeds at fitting inflation to optimal policy, but fails to link inflation to the economic trade-off at the heart of the story. I modify the model to account for data uncertainty and find that doing so ameliorates the existing problems. This suggests that the Fed's model uncertainty is largely overestimated by ignoring data uncertainty. Consequently, now there is an explanation for the rise and fall in inflation: the concurrent rise and fall in the perceived Philips curve trade-off.

Suggested Citation

  • Seth Pruitt, 2008. "Uncertainty over models and data: the rise and fall of American inflation," International Finance Discussion Papers 962, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgif:962
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    References listed on IDEAS

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    1. Boivin, Jean, 2006. "Has U.S. Monetary Policy Changed? Evidence from Drifting Coefficients and Real-Time Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1149-1173, August.
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    3. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
    4. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
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    7. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    8. Howrey, E Philip, 1978. "The Use of Preliminary Data in Econometric Forecasting," The Review of Economics and Statistics, MIT Press, vol. 60(2), pages 193-200, May.
    9. Carboni, Giacomo & Ellison, Martin, 2007. "Learning and the Great Inflation," CEPR Discussion Papers 6250, C.E.P.R. Discussion Papers.
    10. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    11. Alastair Cunningham & Chris Jeffery & George Kapetanios & Vincent Labhard, 2007. "A State Space Approach To The Policymaker's Data Uncertainty Problem," Money Macro and Finance (MMF) Research Group Conference 2006 168, Money Macro and Finance Research Group.
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    Cited by:

    1. Casares, Miguel & Vázquez, Jesús, 2016. "Data Revisions In The Estimation Of Dsge Models," Macroeconomic Dynamics, Cambridge University Press, vol. 20(07), pages 1683-1716, October.
    2. Lubik, Thomas A. & Matthes, Christian, 2016. "Indeterminacy and learning: An analysis of monetary policy in the Great Inflation," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 85-106.
    3. repec:eee:ecmode:v:66:y:2017:i:c:p:156-162 is not listed on IDEAS
    4. Pablo Aguilar & Jesús Vázquez, 2015. "The role of term structure in an estimated DSGE model with learning," Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) 2015007, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    5. Lafuente, Juan A. & Pérez, Rafaela & Ruiz, Jesús, 2014. "Time-varying inflation targeting after the nineties," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 400-408.

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    Keywords

    Uncertainty ; Econometric models ; Economic forecasting;

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