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The use of preliminary data in econometric forecasting: an application with the Bank of Italy Quarterly Model

Author

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  • Fabio Busetti

    () (Bank of Italy, Economic Research Department)

Abstract

This paper considers forecasting by econometric and time series models using preliminary (or provisional) data. The standard practice is to ignore the distinction between provisional and final data. We call the forecasts that ignore such a distinction naive forecasts, which are generated as projections from a correctly specified model using the most recent estimates of the unobserved final figures. It is first shown that in dynamic models a multistepahead naive forecast can achieve a lower mean square error than a single-step-ahead one, intuitively because it is less affected by the measurement noise embedded in the preliminary observations. The best forecasts are obtained by combining, in an optimal way, the information provided by the model with the new information contained in the preliminary data. This can be done in the state space framework, as suggested in the literature. Here we consider two simple methods to combine, in general suboptimally, the two sources of information: modifying the forecast initial conditions via standard regressions and using intercept corrections. The issues are explored with reference to the Italian national accounts data and the Bank of Italy Quarterly Econometric Model (BIQM). A series of simulation experiments with the model show that these methods are quite effective in reducing the extra volatility of prediction due to the use of preliminary data.

Suggested Citation

  • 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.
  • Handle: RePEc:bdi:wptemi:td_437_01
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    References listed on IDEAS

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    6. Faust, Jon & Rogers, John H & Wright, Jonathan H, 2005. "News and Noise in G-7 GDP Announcements," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 403-419, June.
    7. Giampiero M. Gallo & Massimiliano Marcellino, "undated". "Ex Post and Ex Ante Analysis of Provisional Data," Working Papers 141, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
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    9. Patterson, K. D., 2000. "Which vintage of data to use when there are multiple vintages of data?: Cointegration, weak exogeneity and common factors," Economics Letters, Elsevier, vol. 69(2), pages 115-121, November.
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    Citations

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    Cited by:

    1. Harrison, Richard & Kapetanios, George & Yates, Tony, 2005. "Forecasting with measurement errors in dynamic models," International Journal of Forecasting, Elsevier, vol. 21(3), pages 595-607.
    2. Alberto Baffigi & Roberto Golinelli & Giuseppe Parigi, 2002. "Real-time GDP forecasting in the euro area," Temi di discussione (Economic working papers) 456, Bank of Italy, Economic Research and International Relations Area.
    3. George Kapetanios & Tony Yates, 2010. "Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 869-893.
    4. Cecilia Frale & Valentina Raponi, 2011. "Revisions in ocial data and forecasting," Working Papers LuissLab 1194, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    5. 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.

    More about this item

    Keywords

    preliminary data; macroeconomic forecasting; Bank of Italy Quarterly Econometric Model;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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