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Preliminary data and econometric forecasting: an application with the Bank of Italy Quarterly Model

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  • Fabio Busetti

    (Bank of Italy, Research Department, Italy)

Abstract

This paper discusses the use of preliminary data in econometric forecasting. The standard practice is to ignore the distinction between preliminary and final data, the forecasts that do so here being termed naïve forecasts. It is shown that in dynamic models a multistep-ahead naïve forecast can achieve a lower mean square error than a single-step-ahead one, as it is less affected by the measurement noise embedded in the preliminary observations. The minimum mean square error forecasts are obtained by optimally combining the information provided by the model and the new information contained in the preliminary data, which can be done within the state space framework as suggested in numerous papers. Here two simple, in general suboptimal, methods of combining the two sources of information are considered: modifying the forecast initial conditions by means of standard regressions and using intercept corrections. The issues are explored using Italian national accounts data and the Bank of Italy Quarterly Econometric Model. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • Fabio Busetti, 2006. "Preliminary data and econometric forecasting: an application with the Bank of Italy Quarterly Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 1-23.
  • Handle: RePEc:jof:jforec:v:25:y:2006:i:1:p:1-23
    DOI: 10.1002/for.973
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    File URL: http://hdl.handle.net/10.1002/for.973
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    References listed on IDEAS

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    Cited by:

    1. Juan Manuel Julio Román, 2011. "Modeling Data Revisions," BORRADORES DE ECONOMIA 007929, BANCO DE LA REPÚBLICA.
    2. 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.
    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. Golinelli, Roberto & Parigi, Giuseppe, 2014. "Tracking world trade and GDP in real time," International Journal of Forecasting, Elsevier, vol. 30(4), pages 847-862.
    5. Bouwman, Kees E. & Jacobs, Jan P.A.M., 2011. "Forecasting with real-time macroeconomic data: The ragged-edge problem and revisions," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 784-792.
    6. Valentina Raponi & Cecilia Frale, 2014. "Revisions in official data and forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 451-472, August.
    7. R. Golinelli & I. Mammi & A. Musolesi, 2018. "Parameter heterogeneity, persistence and cross-sectional dependence: new insights on fiscal policy reaction functions for the Euro area," Working Papers wp1120, Dipartimento Scienze Economiche, Universita' di Bologna.
    8. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
    9. George Kapetanios & Tony Yates, 2004. "Estimating Time-Variation in Measurement Error from Data Revisions: An Application to Forecasting in Dynamic Models," Working Papers 520, Queen Mary University of London, School of Economics and Finance.

    More about this item

    JEL classification:

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

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