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Preliminary Data and Econometric Forecasting: An Application with the Bank of Italy Quarterly Model

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  • Busetti, Fabio

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 naïve 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 multistep-ahead naïve 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 numerous papers. Here we consider two simple methods to combine, in general sub-optimally, 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. 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

  • Busetti, Fabio, 2004. "Preliminary Data and Econometric Forecasting: An Application with the Bank of Italy Quarterly Model," CEPR Discussion Papers 4382, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:4382
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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. 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.
    3. 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.
    4. Juan Manuel Julio Román, 2011. "Modeling Data Revisions," BORRADORES DE ECONOMIA 007929, BANCO DE LA REPÚBLICA.
    5. 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.
    6. 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.
    7. George Kapetanios, 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.
    8. Golinelli, Roberto & Parigi, Giuseppe, 2014. "Tracking world trade and GDP in real time," International Journal of Forecasting, Elsevier, vol. 30(4), pages 847-862.
    9. 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.

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    More about this item

    Keywords

    Bank of Italy Quarterly Economic Model; macroeconomic forecasting; preliminary data;
    All these keywords.

    JEL classification:

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

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