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What Can We Learn from Comprehensive Data Revisions for Forecasting Inflation: Some US Evidence

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Abstract

The empirical properties of benchmark revisions to key U.S. macroeconomic aggregates are examined. News versus noise impact of revisions is interpreted via the cointegration property of successive benchmark revisions. Cointegration breaks in the last two years before a benchmark revision. Hence, we conclude that there is some information content in benchmark revisions. The last point is illustrated by reporting that inflation forecasts could be improved by the addition of a time series that reflects benchmark revisions to real GDP. Standard backward and forward-looking Phillips curves are used to explore the statistical significance of benchmark revisions.

Suggested Citation

  • Pierre Siklos, 2006. "What Can We Learn from Comprehensive Data Revisions for Forecasting Inflation: Some US Evidence," Working Papers eg0049, Wilfrid Laurier University, Department of Economics, revised 2006.
    • Handle: RePEc:wlu:wpaper:eg0049
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    Cited by:

    1. Jan Jacobs & Jan-Egbert Sturm, 2009. "The information content of KOF indicators on Swiss current account data revisions," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2008(2), pages 161-181.
    2. Clements, Michael P. & Beatriz Galvão, Ana, 2010. "First announcements and real economic activity," European Economic Review, Elsevier, vol. 54(6), pages 803-817, August.
    3. Juan Manuel Julio Román, 2011. "Modeling Data Revisions," Borradores de Economia 7929, Banco de la Republica.
    4. Carlo Altavilla & Matteo Ciccarelli, 2011. "Monetary Policy Analysis in Real-Time. Vintage combination from a real-time dataset," CSEF Working Papers 274, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    5. Garratt, Anthony & Koop, Gary & Mise, Emi & Vahey, Shaun P., 2009. "Real-Time Prediction With U.K. Monetary Aggregates in the Presence of Model Uncertainty," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 480-491.
    6. Cecilia Frale & Valentina Raponi, 2011. "Revisions in ocial data and forecasting," Working Papers LuissLab 1194, Dipartimento di Economia e Finanza, LUISS Guido Carli.

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    More about this item

    Keywords

    cointegration breakdown; real time data; Phillips curve;
    All these keywords.

    JEL classification:

    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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