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The limits of statistical information: How important are GDP revisions in Italy?

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  • Lupi, Claudio

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  • Peracchi, Franco

Abstract

The use of Gross Domestic Product (GDP) as a summary measure of the level of economic activity is pervasive in empirical economics, policy analysis and forecasting. This pervasive role of GDP and its nature of "public good" raises obvious problems of timeliness and accuracy of the data. A striking feature of GDP data (and, more generally, of all national accounts figures) is the presence of "data vintages". That is, the GDP estimate for a specific year or quarter is subject to several revisions after its first release. As a result, both the level and the profile of GDP over a given period may change, sometimes substantially, through time. This paper presents some evidence on the extent of GDP revisions in Italy, with particular emphasis on revisions of the quarterly national accounts series, and compares the Italian evidence with the available evidence from other countries. After discussing some areas in which data revisions can have potentially important consequences, the paper concludes with some policy recommendations.

Suggested Citation

  • Lupi, Claudio & Peracchi, Franco, 2003. "The limits of statistical information: How important are GDP revisions in Italy?," Economics & Statistics Discussion Papers esdp03005, University of Molise, Dept. EGSeI.
  • Handle: RePEc:mol:ecsdps:esdp03005
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    References listed on IDEAS

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    1. G. Cainelli & C. Lupi, 1998. "Aggregazione contemporanea e specificazione econometrica nella stima trimestrale dei conti economici nazionali," Working Papers 319, Dipartimento Scienze Economiche, Universita' di Bologna.
    2. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-673, September.
    3. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    4. Fabio Busetti, 2001. "The use of preliminary data in econometric forecasting: an application with the Bank of Italy Quarterly Model," Temi di discussione (Economic working papers) 437, Bank of Italy, Economic Research and International Relations Area.
    5. Cainelli, Giulio & Lupi, Claudio, 1999. "The Choice of the Aggregation Level in the Estimation of Quarterly National Accounts," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 45(4), pages 483-492, December.
    6. Bordignon, Silvano & Trivellato, Ugo, 1989. "The Optimal Use of Provisional Data in Forecasting with Dynamic Model s," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(2), pages 275-286, April.
    7. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    8. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    9. Francis X. Diebold & Glenn D. Rudebusch, 1989. "Forecasting output with the composite leading index: an ex ante analysis," Finance and Economics Discussion Series 90, Board of Governors of the Federal Reserve System (U.S.).
    10. de Jong, Piet, 1987. "Rational Economic Data Revisions," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 539-548, October.
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    Citations

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    Cited by:

    1. Christian Ragacs & Martin Schneider, 2009. "Why did we fail to predict GDP during the last cycle? A breakdown of forecast errors for Austria," Working Papers 151, Oesterreichische Nationalbank (Austrian Central Bank).
    2. Golinelli, Roberto & Parigi, Giuseppe, 2005. "Short-Run Italian GDP Forecasting and Real-Time Data," CEPR Discussion Papers 5302, C.E.P.R. Discussion Papers.
    3. Golinelli, Roberto & Parigi, Giuseppe, 2008. "Real-time squared: A real-time data set for real-time GDP forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 368-385.
    4. repec:onb:oenbwp:y::i:151:b:1 is not listed on IDEAS

    More about this item

    Keywords

    Data revisions; National accounts; Real-time datasets.;

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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